[
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.08.038",
"title": "Ed\n itorial Commentary\n : Experts in Shoulder Surgery Do Not Consistently Detect Artificial Intelligence–Generated Scientific Abstracts",
"page": "925-926"
},
{
"journal": "Arthroscopy",
"year": 2021,
"doi": "10.1016/j.arthro.2021.12.012",
"title": "Duration of Care and Operative Time Are the Primary Drivers of Total Charges After Ambulatory Hip Arthroscopy: A Machine Learning Analysis",
"page": "2204"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.06.045",
"title": "Identification of ChatGPT‐Generated Abstracts Within Shoulder and Elbow Surgery Poses a Challenge for Reviewers",
"page": "916"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.06.021",
"title": "The Large Language Model ChatGPT‐4 Exhibits Excellent Triage Capabilities and Diagnostic Performance for Patients Presenting With Various Causes of Knee Pain",
"page": "1438"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.05.027",
"title": "Transformer‐Based Multilabel Deep Learning Model Is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on Magnetic Resonance Imaging and Improving Clinicians’ Diagnostic Accuracy for Rotational Chronic Ankle Instability",
"page": "574"
},
{
"journal": "Arthroscopy",
"year": 2025,
"doi": "10.1016/j.arthro.2025.01.039",
"title": "ChatGPT and Gemini Are Not Consistently Concordant With the 2020 American Academy of Orthopaedic Surgeons Clinical Practice Guidelines When Evaluating Rotator Cuff Injury",
"page": "2753-2757"
},
{
"journal": "Arthroscopy",
"year": 2026,
"doi": "10.1002/arj.70160",
"title": "Editorial Commentary\n : Traveling to A New Dimension in Anterior Cruciate Ligament Injury Risk Stratification: Holistic 3‐Dimensional Knee Phenotyping via Automated Artificial Intelligence Pipelines",
"page": "1184-1186"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.08.023",
"title": "ChatGPT, Bard, and Bing Chat Are Large Language Processing Models That Answered Orthopaedic In‐Training Examination Questions With Similar Accuracy to First‐Year Orthopaedic Surgery Residents",
"page": "557-562"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.07.040",
"title": "Currently Available Large Language Models Do Not Provide Musculoskeletal Treatment Recommendations That Are Concordant With Evidence‐Based Clinical Practice Guidelines",
"page": "263"
},
{
"journal": "Arthroscopy",
"year": 2020,
"doi": "10.1016/j.arthro.2020.09.012",
"title": "Diagnostic Performance of Artificial Intelligence for Detection of Anterior Cruciate Ligament and Meniscus Tears: A Systematic Review",
"page": "771-781"
},
{
"journal": "Arthroscopy",
"year": 2021,
"doi": "10.1016/j.arthro.2020.12.231",
"title": "Editorial Commentary:\n Predicting Satisfaction After Hip Arthroscopy Using Machine Learning: What Do Treadmills and Black Boxes Have to Do With Arthroscopy?",
"page": "1152-1154"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2023.07.042",
"title": "Editorial Commentary:\n Artificial Intelligence Models Show Impressive Results for Musculoskeletal Pathology Detection",
"page": "579-580"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.08.029",
"title": "Editorial Commentary\n : Large Language Models Like ChatGPT Show Promise, but Clinical Use of Artificial Intelligence Requires Physician Partnership",
"page": "1448-1450"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.03.009",
"title": "Editorial Commentary:\n Evaluation for Cartilage Lesions on Magnetic Resonance Imaging Continues to Improve: Artificial Intelligence Applications May Result in Higher Sensitivity and Specificity",
"page": "2774-2775"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.06.017",
"title": "ChatGPT Provides Satisfactory but Occasionally Inaccurate Answers to Common Patient Hip Arthroscopy Questions",
"page": "1337-1347"
},
{
"journal": "Arthroscopy",
"year": 2026,
"doi": "10.1002/arj.70481",
"title": "Arthroscopic Images Predict Tendon Integrity After Arthroscopic Rotator Cuff Repair Using a Deep Learning Model"
},
{
"journal": "Arthroscopy",
"year": 2026,
"doi": "10.1002/arj.70480",
"title": "Artificial Intelligence Accurately Assists in Billing for Orthopaedic Lower Extremity Surgery: Performance of the Mistral‐NeMo Language Model"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.08.036",
"title": "ChatGPT Can Offer At Least Satisfactory Responses to Common Patient Questions Regarding Hip Arthroscopy",
"page": "1806-1827"
},
{
"journal": "Arthroscopy",
"year": 2026,
"doi": "10.1002/arj.70161",
"title": "Distinct 3‐Dimensional Anatomic Patterns Including Flatter Surfaces and Greater Sagittal Inclinations of Intra‐articular Structures Are Reliably Identified Through an Artificial Intelligence‐Based Pipeline in Anterior Cruciate Ligament‐Injured Knees",
"page": "1172-1183"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.06.032",
"title": "Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30‐Day Complications Following Anterior Cruciate Ligament Reconstruction",
"page": "777"
},
{
"journal": "Arthroscopy",
"year": 2023,
"doi": "10.1016/j.arthro.2023.12.009",
"title": "ChatGPT Responses to Common Questions About Anterior Cruciate Ligament Reconstruction Are Frequently Satisfactory",
"page": "2058-2066"
},
{
"journal": "Arthroscopy",
"year": 2021,
"doi": "10.1016/j.arthro.2021.12.030",
"title": "Machine Learning Algorithms Predict Achievement of Clinically Significant Outcomes After Orthopaedic Surgery: A Systematic Review",
"page": "2090-2105"
},
{
"journal": "Arthroscopy",
"year": 2023,
"doi": "10.1016/j.arthro.2023.08.010",
"title": "A Deep Learning Model Enhances Clinicians' Diagnostic Accuracy to More Than 96% for Anterior Cruciate Ligament Ruptures on Magnetic Resonance Imaging",
"page": "1197-1205"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2023.09.012",
"title": "SONNET #29888: ChatGPT Finds Poetry in Anterior Cruciate Ligament Reconstruction and Return to Sport",
"page": "197-198"
},
{
"journal": "Arthroscopy",
"year": 2021,
"doi": "10.1016/j.arthro.2021.02.032",
"title": "Editorial Commentary:\n Personalized Hip Arthroscopy Outcome Prediction Using Machine Learning—The Future Is Here",
"page": "1498-1502"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.12.025",
"title": "Machine Learning Model Identifies Preoperative Opioid Use, Male Sex, and Elevated Body Mass Index as Predictive Factors for Prolonged Opioid Consumption Following Arthroscopic Meniscal Surgery",
"page": "1505-1511"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.01.017",
"title": "ChatGPT Provides Unsatisfactory Responses to Frequently Asked Questions Regarding Anterior Cruciate Ligament Reconstruction",
"page": "2067"
},
{
"journal": "Arthroscopy",
"year": 2026,
"doi": "10.1002/arj.70016",
"title": "Large Language Model Predicts Surgeon Recommendations for Imaging and Surgery for Patients Presenting for Knee and Shoulder Complaints With 70% and 81% Accuracy Using Previsit Questionnaire Responses",
"page": "185-193"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.11.040",
"title": "A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle–Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis",
"page": "1493"
},
{
"journal": "Arthroscopy",
"year": 2023,
"doi": "10.1016/j.arthro.2023.07.048",
"title": "Evaluation High‐Quality of Information from ChatGPT (Artificial Intelligence—Large Language Model) Artificial Intelligence on Shoulder Stabilization Surgery",
"page": "726"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.01.041",
"title": "Editorial Commentary:\n Machine Learning Can Indicate Hip Arthroscopy Procedures, Predict Postoperative Improvement, and Estimate Costs",
"page": "2217-2218"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.03.037",
"title": "Ultrasound With Artificial Intelligence Models Predicted Palmer 1B Triangular Fibrocartilage Complex Injuries",
"page": "2417-2424"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.05.010",
"title": "Editorial Commentary:\n Machine Learning in Orthopaedics: Venturing Into the Valley of Despair",
"page": "2767-2768"
},
{
"journal": "Arthroscopy",
"year": 2021,
"doi": "10.1016/j.arthro.2021.01.005",
"title": "Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy",
"page": "1488-1497"
},
{
"journal": "Arthroscopy",
"year": 2024,
"doi": "10.1016/j.arthro.2024.09.020",
"title": "Artificial Intelligence Large Language Models Address Anterior Cruciate Ligament Reconstruction: Superior Clarity and Completeness by Gemini Compared With ChatGPT‐4 in Response to American Academy of Orthopaedic Surgeons Clinical Practice Guidelines",
"page": "2002-2008"
},
{
"journal": "Arthroscopy",
"year": 2025,
"doi": "10.1016/j.arthro.2025.06.020",
"title": "Editorial Commentary\n : Shifting From Redundancy to Rigor in Orthopaedic Large Language Model Research",
"page": "4946-4949"
},
{
"journal": "Arthroscopy",
"year": 2020,
"doi": "10.1016/j.arthro.2020.11.027",
"title": "Machine Learning Algorithms Predict Clinically Significant Improvements in Satisfaction After Hip Arthroscopy",
"page": "1143-1151"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.05.016",
"title": "Editorial Commentary\n : Artificial Intelligence for the Wrist: Moving to the Forefront of Diagnostic Imaging for Triangular Fibrocartilage Complex Injury",
"page": "2425-2426"
},
{
"journal": "Arthroscopy",
"year": 2022,
"doi": "10.1016/j.arthro.2022.04.014",
"title": "Meaningless Applications and Misguided Methodologies in Artificial Intelligence–Related Orthopaedic Research Propagates Hype Over Hope",
"page": "2761-2766"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003413",
"title": "Artificial Intelligence Shows Limited Success in Improving Readability Levels of Spanish-language Orthopaedic Patient Education Materials",
"page": "1185-1192"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000848",
"title": "What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review",
"page": "2482-2491"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2021,
"doi": "10.1097/corr.0000000000001679",
"title": "CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?",
"page": "1497-1505"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003084",
"title": "Editorial: Uses of Generative Artificial Intelligence in Clinical Orthopaedics and Related Research ®—An Update",
"page": "909-911"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003331",
"title": "Reply to the Letter to the Editor: Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?",
"page": "548-549"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002397",
"title": "Letter to the Editor: Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis",
"page": "2277-2277"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003651",
"title": "Letter to the Editor: Development of Machine Learning–based Algorithms to Predict the 2- and 5-year Risk of TKA After Tibial Plateau Fracture Treatment",
"page": "2001-2002"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003417",
"title": "Letter to the Editor: “Dr. AI Will See You Now“: How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?",
"page": "959-959"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003293",
"title": "Editor’s Spotlight/Take 5: “Dr. AI Will See You Now”: How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?",
"page": "2094-2097"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002619",
"title": "Not the Last Word: ChatGPT Can’t Perform Orthopaedic Surgery",
"page": "651-655"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002828",
"title": "CORR Insights®: Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer",
"page": "2257-2259"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001227",
"title": "Reply to the Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?",
"page": "1376-1377"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003527",
"title": "Reply to the Letter to the Editor: Artificial Intelligence Shows Limited Success in Improving Readability Levels of Spanish-language Orthopaedic Patient Education Materials",
"page": "1567-1568"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002869",
"title": "Letter to the Editor: CORR Insights®: Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT",
"page": "2299-2299"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000873",
"title": "Value-based Healthcare: Can Artificial Intelligence Provide Value in Orthopaedic Surgery?",
"page": "1777-1780"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002704",
"title": "Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT",
"page": "1623-1630"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000912",
"title": "CORR Insights®: What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review",
"page": "2492-2494"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2026,
"doi": "10.1097/corr.0000000000003969",
"title": "Editor’s Spotlight/Take 5: Few FDA Approved AI/ML Orthopaedic Devices Have EU MDR Equivalents or Peer-Reviewed Validation",
"page": "1056-1059"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2026,
"doi": "10.1097/corr.0000000000003791",
"title": "Not the Last Word: I Was Wrong—AI May Be Coming for Orthopaedic Jobs, Too"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003788",
"title": "Few FDA Approved AI/ML Orthopaedic Devices Have EU MDR Equivalents or Peer-Reviewed Validation",
"page": "1060-1067"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002771",
"title": "Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer",
"page": "2247-2256"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002105",
"title": "Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis",
"page": "1271-1284"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002385",
"title": "Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types",
"page": "580-588"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003455",
"title": "Reply to the Letter to the Editor: “Dr. AI Will See You Now”: How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?",
"page": "960-961"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002612",
"title": "Can a Deep Learning Algorithm Improve Detection of Occult Scaphoid Fractures in Plain Radiographs? A Clinical Validation Study",
"page": "1828-1835"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2026,
"doi": "10.1097/corr.0000000000003828",
"title": "Machine Learning–driven Probability Calculators Can Accurately Predict 1-year Mortality After Proximal Humerus Fractures in Patients Over the Age of 65 Years",
"page": "1020-1032"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000787",
"title": "Editor’s Spotlight/Take 5: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?",
"page": "1262-1266"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2026,
"doi": "10.1097/corr.0000000000003909",
"title": "CORR Insights®: Machine Learning–driven Probability Calculators Can Accurately Predict 1-year Mortality After Proximal Humerus Fractures in Patients Over the Age of 65 Years",
"page": "1033-1035"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002139",
"title": "CORR Insights®: Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis",
"page": "1285-1286"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002705",
"title": "Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes",
"page": "1745-1759"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002663",
"title": "CORR Insights®: Can a Deep Learning Algorithm Improve Detection of Occult Scaphoid Fractures in Plain Radiographs? A Clinical Validation Study",
"page": "1836-1838"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003046",
"title": "CORR Insights®: Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty",
"page": "1483-1484"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003559",
"title": "Editor’s Spotlight/Take 5: Artificial Intelligence Shows Limited Success in Improving Readability Levels of Spanish-language Orthopaedic Patient Education Materials",
"page": "1180-1184"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001344",
"title": "CORR Insights®: What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?",
"page": "2364-2366"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003514",
"title": "CORR Insights®: Development of Machine Learning–based Algorithms to Predict the 2- and 5-year Risk of TKA After Tibial Plateau Fracture Treatment",
"page": "1744-1745"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003470",
"title": "Letter to the Editor: Artificial Intelligence Shows Limited Success in Improving Readability Levels of Spanish-language Orthopaedic Patient Education Materials",
"page": "1566-1566"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001352",
"title": "CORR Insights®: Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?",
"page": "2660-2662"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001415",
"title": "CORR Insights®: Does Artificial Intelligence Outperform Natural Intelligence in Interpretation of Musculoskeletal Radiological Studies? A Systematic Review",
"page": "2765-2767"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003304",
"title": "Letter to the Editor: Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?",
"page": "547-547"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002741",
"title": "CORR Insights®: Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents Versus ChatGPT",
"page": "1631-1633"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001255",
"title": "Reply to the Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?",
"page": "1378-1379"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002463",
"title": "CORR Insights®: Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types",
"page": "589-591"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003234",
"title": "“Dr. AI Will See You Now”: How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?",
"page": "2098-2106"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003235",
"title": "CORR ® Curriculum—Orthopaedic Education: Artificial Intelligence and Surgical Assessment",
"page": "1760-1762"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002658",
"title": "Editorial: Artificial Intelligence Applications and Scholarly Publication in Orthopaedic Surgery",
"page": "1055-1056"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002725",
"title": "Letter to the Editor: Editorial: Artificial Intelligence Applications and Scholarly Publication in Orthopaedic Surgery",
"page": "1652-1653"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000601",
"title": "Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?",
"page": "452-460"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001318",
"title": "Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?",
"page": "2653-2659"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001263",
"title": "What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?",
"page": "2351-2363"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003442",
"title": "Development of Machine Learning–based Algorithms to Predict the 2- and 5-year Risk of TKA After Tibial Plateau Fracture Treatment",
"page": "1731-1743"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2023,
"doi": "10.1097/corr.0000000000002777",
"title": "CORR Insights®: Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes",
"page": "1760-1762"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2019,
"doi": "10.1097/corr.0000000000000687",
"title": "Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?",
"page": "1267-1279"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003018",
"title": "Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty",
"page": "1472-1482"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003269",
"title": "CORR Insights®: Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?",
"page": "316-317"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001360",
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"page": "2751-2764"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2020,
"doi": "10.1097/corr.0000000000001226",
"title": "Letter to the Editor: Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?",
"page": "1374-1375"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2022,
"doi": "10.1097/corr.0000000000002398",
"title": "Reply to the Letter to the Editor: Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis",
"page": "2278-2279"
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{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2024,
"doi": "10.1097/corr.0000000000003263",
"title": "Is Information About Musculoskeletal Malignancies From Large Language Models or Web Resources at a Suitable Reading Level for Patients?",
"page": "306-315"
},
{
"journal": "Clinical Orthopaedics and Related Research",
"year": 2025,
"doi": "10.1097/corr.0000000000003660",
"title": "Are Artificial Intelligence Models Reliable for Clinical Application in Pediatric Fracture Detection on Radiographs? A Systematic Review and Meta-analysis",
"page": "371-385"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2025.07.025",
"title": "Deep learning for automated 3D assessment of rotator cuff muscle atrophy and fat infiltration prior to total shoulder arthroplasty",
"page": "e490-e504"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2025.07.034",
"title": "Response to Baek et al. regarding: “Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty”",
"page": "e518-e520"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2019,
"doi": "10.1016/j.jse.2019.05.017",
"title": "Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty",
"page": "e410-e421"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2023.03.001",
"title": "Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning",
"page": "e437-e450"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2022,
"doi": "10.1016/j.jse.2022.01.041",
"title": "Artificial Intelligence-Based Classification Of Proximal Humeral Fractures In Plain Shoulder Radiographs, Performance Of A Convolutional Neural Network Fracture Classifier",
"page": "e146"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2021,
"doi": "10.1016/j.jse.2020.07.042",
"title": "Using machine learning to predict clinical outcomes after shoulder arthroplasty with a minimal feature set",
"page": "e225-e236"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.10.028",
"title": "A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors",
"page": "1792-1800"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2025.07.035",
"title": "Letter to the editor regarding Ritter et al: “Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty”",
"page": "e513-e517"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2025.03.010",
"title": "Automatic determination of the resection plane for shoulder arthroplasty in arthritic humeri: a deep learning model",
"page": "e1301-e1309"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2023.12.009",
"title": "Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning",
"page": "1733-1739"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2026.06.023",
"title": "Prediction of acromial and scapular spine fractures after reverse total shoulder arthroplasty using machine learning: a retrospective cohort study"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.04.022",
"title": "The relationship between design-based lateralization, humeral bearing design, polyethylene angle, and patient-related factors on surgical complications after reverse shoulder arthroplasty: a machine learning analysis",
"page": "462-472"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2023.07.017",
"title": "Re-tear after arthroscopic rotator cuff tear surgery: risk analysis using machine learning",
"page": "815-822"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2021,
"doi": "10.1016/j.jse.2021.01.021",
"title": "Validation of a machine learning–derived clinical metric to quantify outcomes after total shoulder arthroplasty",
"page": "2211-2224"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2023.11.014",
"title": "Do ChatGPT and Google differ in answers to commonly asked patient questions regarding total shoulder and total elbow arthroplasty?",
"page": "e429-e437"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2021,
"doi": "10.1016/j.jse.2020.05.013",
"title": "Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty",
"page": "e50-e59"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2022.09.029",
"title": "Exactech Equinoxe anatomic versus reverse total shoulder arthroplasty for primary osteoarthritis: case controlled comparisons using the machine learning–derived Shoulder Arthroplasty Smart score",
"page": "793-802"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2026.07.004",
"title": "Development of convolutional neural networks for classification and characterization of proximal humerus fractures on computed tomography"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2023.08.005",
"title": "Evaluating the fairness and accuracy of machine learning–based predictions of clinical outcomes after anatomic and reverse total shoulder arthroplasty",
"page": "888-899"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2022,
"doi": "10.1016/j.jse.2022.07.013",
"title": "Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning",
"page": "2449-2456"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2023.02.066",
"title": "Deep Learning-Based Screening Tool For Rotator Cuff Tears On Shoulder Radiography",
"page": "e260"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.12.048",
"title": "Assessment and comparison of artificial intelligence–generated information regarding shoulder arthroplasty from multiple interfaces",
"page": "2216-2223"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2022,
"doi": "10.1016/j.jse.2022.01.030",
"title": "Deep Learning Provides A Reliable Automatic Quantification Of Rotator Cuff Muscle Degeneration Derived From Preoperative Arthroplasty Planification Shoulder CT Images",
"page": "e142-e143"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2023.03.028",
"title": "Artificial intelligence for automated identification of total shoulder arthroplasty implants",
"page": "2115-2122"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.08.008",
"title": "The future of Food and Drug Administration regulation on artificial intelligence–enabled medical devices: an orthopedic surgeon’s guide",
"page": "260-264"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.12.033",
"title": "Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis",
"page": "2224-2238"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.11.030",
"title": "Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques",
"page": "e645-e655"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2020,
"doi": "10.1016/j.jse.2019.10.031",
"title": "Let’s “recall” sensitivity and specificity: Letter to the Editor regarding Gowd et al: “Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty”",
"page": "e98-e99"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2025.10.011",
"title": "Artificial intelligence to automatically identify reverse shoulder arthroplasty implant brands on postoperative radiographs including uncertainty quantification",
"page": "1318-1326"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2024.01.033",
"title": "Artificial intelligence in shoulder and elbow surgery: overview of current and future applications",
"page": "1633-1641"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2023.02.030",
"title": "Effect Of Machine Learning Prediction On Surgical Decision Making For Shoulder Arthroplasty: A Multi-Surgeon Study",
"page": "e247"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.07.006",
"title": "Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty",
"page": "e141-e151"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2025,
"doi": "10.1016/j.jse.2024.08.025",
"title": "Can ChatGPT reliably answer the most common patient questions regarding total shoulder arthroplasty?",
"page": "e254-e264"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2020,
"doi": "10.1016/j.jse.2020.04.009",
"title": "The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty",
"page": "2385-2394"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2023.09.021",
"title": "Deep learning to automatically classify very large sets of preoperative and postoperative shoulder arthroplasty radiographs",
"page": "773-780"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2024,
"doi": "10.1016/j.jse.2024.02.029",
"title": "ChatGPT performance on the American Shoulder and Elbow Surgeons maintenance of certification exam",
"page": "1888-1893"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2026,
"doi": "10.1016/j.jse.2026.02.005",
"title": "Automated multiclass bone segmentation using deep learning: implications for templating in radial head replacement",
"page": "e818-e829"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2023,
"doi": "10.1016/j.jse.2023.05.006",
"title": "Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation",
"page": "e624-e635"
},
{
"journal": "Journal of Shoulder and Elbow Surgery",
"year": 2021,
"doi": "10.1016/j.jse.2020.09.007",
"title": "Development of supervised machine learning algorithms for prediction of satisfaction at 2 years following total shoulder arthroplasty",
"page": "e290-e299"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.02.006",
"title": "Comparison of predictive models for knee pain and analysis of individual and physical activity variables using interpretable machine learning",
"page": "146-153"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.02.014",
"title": "Artificial intelligence and machine learning in knee arthroplasty",
"page": "28-49"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104427",
"title": "Concordance of ChatGPT, Gemini, Claude, and OpenEvidence with the 2024 AAOS guidelines on acute isolated meniscal pathology",
"page": "104427"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.06.005",
"title": "Re: “Diagnostic accuracy of ChatGPT-4 in orthopedic oncology: a comparative study with residents”",
"page": "659-660"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.03.008",
"title": "“Managing class imbalance in the training of a large language model to predict patient selection for total knee arthroplasty”: Comment★",
"page": "349-350"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.05.011",
"title": "Developing and validating a machine learning model to predict chronic pain following total knee arthroplasty",
"page": "52-65"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.10.022",
"title": "The need for better quality studies: A systematic scoping review of current utility of artificial intelligence in orthopaedics and research gaps in the knee joint",
"page": "104268"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.04.004",
"title": "Diagnostic accuracy of ChatGPT-4 in orthopedic oncology: a comparative study with residents",
"page": "153-160"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.11.008",
"title": "Artificial Intelligence (AI) and surgery of the knee",
"page": "104282"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104579",
"title": "Large language models as sources of patient information on robotic knee arthroplasty: a comparative evaluation",
"page": "104579"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.10.024",
"title": "Deep learning-enabled segmentation of knee cartilage in conventional magnetic resonance images: Internal and external validation of different models",
"page": "104270"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.02.001",
"title": "Deep learning for tibial plateau fracture detection and classification",
"page": "81-89"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104617",
"title": "Response to the Letter to the Editor regarding “Quality assessment of YouTube videos on total knee arthroplasty: a comparative evaluation of orthopaedic surgeons and artificial intelligence models”",
"page": "104617"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104612",
"title": "Comment on “Quality assessment of YouTube videos on total knee arthroplasty: a comparative evaluation of orthopedic surgeons and artificial intelligence models”",
"page": "104612"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.02.009",
"title": "A deep learning and statistical shape modeling-based method for assessing intercondylar notch volume in anterior cruciate ligament reconstruction",
"page": "71-80"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104635",
"title": "Comparative quality, accuracy, and readability of large language model responses to patient questions about robotic-assisted total knee arthroplasty",
"page": "104635"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104516",
"title": "Evaluating generative AI responses at the 3-month timepoint following total knee arthroplasty",
"page": "104516"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.11.016",
"title": "Improving patellofemoral pain assessment with weight-bearing computed tomography and machine learning using three-dimensional knee joint metrics",
"page": "104290"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.08.015",
"title": "What’s new in artificial intelligence applications for knee surgery?",
"page": "A1-A2"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.05.017",
"title": "Towards streamlining orthopedic consultations: Machine learning classification of knee diagnosis groups via computer-assisted history taking",
"page": "241-248"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.06.018",
"title": "Can ChatGPT provide personalised responses for frequently asked questions regarding anterior cruciate ligament reconstruction?",
"page": "495-502"
},
{
"journal": "The Knee",
"year": 2024,
"doi": "10.1016/j.knee.2024.03.008",
"title": "Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence",
"page": "128-137"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104327",
"title": "Artificial intelligence in knee osteoarthritis imaging and total knee arthroplasty: advances, challenges, and segmentation methods – A review",
"page": "104327"
},
{
"journal": "The Knee",
"year": 2023,
"doi": "10.1016/j.knee.2023.03.010",
"title": "Predicting hip-knee-ankle and femorotibial angles from knee radiographs with deep learning",
"page": "281-288"
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{
"journal": "The Knee",
"year": 2023,
"doi": "10.1016/j.knee.2023.08.018",
"title": "The development of machine learning algorithms that can predict patients satisfaction using baseline characteristics, and preoperative and operative factors of total knee arthroplasty",
"page": "253-261"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104524",
"title": "Evaluation of the performance of large language models in the management of hip and knee osteoarthritis: analysis of EULAR 2023 recommendations",
"page": "104524"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104491",
"title": "Response to the Letter to the Editor regarding “Comparative evaluation of large language models for patient education after total knee arthroplasty”",
"page": "104491"
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{
"journal": "The Knee",
"year": 2026,
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"title": "Letter to the editor regarding “Comparative evaluation of large language models for patient education after total knee arthroplasty”",
"page": "104487"
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{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.02.007",
"title": "Managing class imbalance in the training of a large language model to predict patient selection for total knee arthroplasty: Results from the Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project",
"page": "1-8"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.03.005",
"title": "ChatGPT versus expert arthroplasty surgeons in total knee arthroplasty patient counseling",
"page": "12-17"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.10.021",
"title": "The use of machine learning in predicting anterior cruciate ligament injury: a systematic review and meta-analysis",
"page": "104267"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104365",
"title": "Knee osteoarthritis classification using Optimized Granger Causality Inspired Graph Neural Network with Accelerated Model-agnostic Explanations",
"page": "104365"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.104316",
"title": "Response to letter to the editor to: “ChatGPT delivers satisfactory responses to the most frequent questions on meniscus surgery”",
"page": "104316"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104618",
"title": "Artificial intelligence–based versus conventional preoperative templating for tibial and femoral component size estimation in total knee arthroplasty: a systematic review and meta-analysis",
"page": "104618"
},
{
"journal": "The Knee",
"year": 2025,
"doi": "10.1016/j.knee.2025.06.007",
"title": "ChatGPT-4.0 and DeepSeek-R1 does not yet provide clinically supported answers for knee osteoarthritis",
"page": "386-396"
},
{
"journal": "The Knee",
"year": 2020,
"doi": "10.1016/j.knee.2019.11.020",
"title": "Automated detection & classification of knee arthroplasty using deep learning",
"page": "535-542"
},
{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104525",
"title": "Machine learning-based clinical tool for identifying factors associated with symptomatic knee osteoarthritis: the Nagahama study",
"page": "104525"
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{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2026.104529",
"title": "Quality assessment of YouTube videos on total knee arthroplasty: a comparative evaluation of orthopedic surgeons and artificial intelligence models",
"page": "104529"
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{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.11.002",
"title": "How accurately do large language models answer patient questions on anterior cruciate ligament tears? A comparative study",
"page": "104276"
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{
"journal": "The Knee",
"year": 2026,
"doi": "10.1016/j.knee.2025.104317",
"title": "Letter to the editor regarding “ChatGPT delivers satisfactory responses to the most frequent questions on meniscus surgery”",
"page": "104317"
},
{
"journal": "The Knee",
"year": 2023,
"doi": "10.1016/j.knee.2022.12.007",
"title": "Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals",
"page": "115-123"
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{
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},
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},
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{
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},
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"doi": "10.1016/j.arth.2023.03.039",
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"doi": "10.1016/j.arth.2019.06.018",
"title": "Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring",
"page": "2204-2209"
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{
"journal": "Journal of Arthroplasty",
"year": 2019,
"doi": "10.1016/j.arth.2019.01.036",
"title": "Letter to the Editor on “Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model”",
"page": "1041"
},
{
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"doi": "10.1016/j.arth.2023.01.065",
"title": "Internal and External Validation of the Generalizability of Machine Learning Algorithms in Predicting Non-home Discharge Disposition Following Primary Total Knee Joint Arthroplasty",
"page": "1973-1981"
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{
"journal": "Journal of Arthroplasty",
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"doi": "10.1016/j.arth.2024.04.083",
"title": "Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment",
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{
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"title": "The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty",
"page": "2075-2080"
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{
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"title": "Joint Track Machine Learning: An Autonomous Method of Measuring Total Knee Arthroplasty Kinematics From Single-Plane X-Ray Images",
"page": "2068-2074"
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{
"journal": "Journal of Arthroplasty",
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"doi": "10.1016/j.arth.2022.08.037",
"title": "WITHDRAWN: A deep learning framework for diagnosing periprosthetic joint infections using X-ray images: a discovery and validation study"
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{
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"year": 2023,
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"title": "The Impact of Machine Learning on Total Joint Arthroplasty Patient Outcomes: A Systemic Review",
"page": "2085-2095"
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{
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"year": 2023,
"doi": "10.1016/j.arth.2022.10.017",
"title": "Identifying Modifiable Cost Drivers of Outpatient Unicompartmental Knee Arthroplasty With Machine Learning",
"page": "2051-2059.e2"
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"doi": "10.1016/j.arth.2020.12.046",
"title": "Response to Letter to the Editor on “Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip”",
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"title": "Predicting Patient-Reported Outcome Measures, Satisfaction, Health-care Utilization, Mortality, and Return to Work After Total Knee Arthroplasty Using Machine Learning: A 14,900-Patient Study"
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"doi": "10.1016/j.arth.2020.07.026",
"title": "Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?",
"page": "112-117.e6"
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{
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"doi": "10.1016/j.arth.2019.07.024",
"title": "Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial",
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"journal": "The Spine Journal",
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"year": 2026,
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"title": "99. Identifying drivers of 90-day readmission after spinal fusion in pediatric neuromuscular scoliosis: a SHAP-supported machine learning analysis",
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"year": 2023,
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"title": "2024 NASS Presidential Address: Empathy vs Efficiency in the age of AI in spine care",
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"journal": "The Spine Journal",
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"title": "Updated external validation of the SORG machine learning algorithms for prediction of ninety-day and one-year mortality after surgery for spinal metastasis",
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"doi": "10.1016/j.spinee.2019.06.024",
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"year": 2026,
"doi": "10.1016/j.spinee.2026.06.149",
"title": "130. Development and validation of a U-Net-based deep learning model for intact and fractured metastatic vertebral body auto-segmentation on radiation planning CTs",
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"year": 2025,
"doi": "10.1016/j.spinee.2024.10.001",
"title": "Development of machine learning model for predicting prolonged operation time in lumbar stenosis undergoing posterior lumbar interbody fusion: a multicenter study",
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"year": 2026,
"doi": "10.1016/j.spinee.2026.04.010",
"title": "Deep learning magnetic resonance imaging algorithm for differentiating metastatic vertebral fractures",
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"year": 2022,
"doi": "10.1016/j.spinee.2022.01.004",
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"year": 2026,
"doi": "10.1016/j.spinee.2026.06.084",
"title": "66. AI-driven radiographic and biomechanical analysis for cervical total disc replacement: a preliminary multicohort investigation",
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"year": 2025,
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"doi": "10.1016/j.spinee.2021.05.333",
"title": "P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion",
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"year": 2022,
"doi": "10.1016/j.spinee.2022.06.249",
"title": "229. Using machine learning (ML) models to predict risk of venous thromboembolism (VTE) following spine surgery",
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"year": 2023,
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"year": 2024,
"doi": "10.1016/j.spinee.2024.02.002",
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"year": 2024,
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"title": "Differentiating spinal pathologies by deep learning approach",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
"year": 2026,
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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"title": "Machine learning model outperforms the ACS Risk Calculator in predicting non‐home discharge following primary total knee arthroplasty",
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"title": "Pre‐injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
"year": 2024,
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"title": "Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
"year": 2025,
"doi": "10.1002/ksa.70053",
"title": "Lower baseline scores best predict achievement of the minimal clinically important difference after hip arthroscopy: A machine learning analysis from the Femoroacetabular Impingement RandomiSed Controlled Trial and embedded prospective cohort",
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{
"journal": "Knee Surgery Sports Traumatology Arthroscopy",
"year": 2026,
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"title": "Biomechanical phenotypes of 90° change of direction in football players: Unsupervised machine learning in anterior cruciate ligament injury prevention"
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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"journal": "Knee Surgery Sports Traumatology Arthroscopy",
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{
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"page": "2000-2006"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10051-x",
"title": "Artificial intelligence–driven data expansion for the validation of spinopelvic parameter correlations in asymptomatic subjects"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09910-4",
"title": "Clinical evaluation of deep learning accelerated lumbar T2-weighted and fat-suppressed MRI sequences"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08396-2",
"title": "Artificial intelligence in predicting postoperative heterotopic ossification following anterior cervical disc replacement",
"page": "4082-4091"
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{
"journal": "European Spine Journal",
"year": 2021,
"doi": "10.1007/s00586-021-06914-0",
"title": "Use of residual neural network for the detection of ossification of the posterior longitudinal ligament on plain cervical radiography",
"page": "2185-2190"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-08038-z",
"title": "Answer to the letter to the editor of H. Daungsupawong et al. concerning \"Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?” by R. Stroop et al. (Eur Spine J [2023]; doi:10.1007/s00586-023–07975-z)",
"page": "362-362"
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{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08609-8",
"title": "An integrative nomogram based on MRI radiomics and clinical characteristics for prognosis prediction in cervical spinal cord Injury",
"page": "1164-1176"
},
{
"journal": "European Spine Journal",
"year": 2019,
"doi": "10.1007/s00586-019-05944-z",
"title": "Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach",
"page": "951-960"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09106-2",
"title": "A novel artificial Intelligence-Based model for automated Lenke classification in adolescent idiopathic scoliosis",
"page": "3929-3939"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07954-4",
"title": "Pre-operative prediction of post-operative urinary retention in lumbar surgery: a prospective validation of machine learning model",
"page": "3868-3874"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09075-6",
"title": "Research on a deep learning-based model for measurement of X-ray imaging parameters of atlantoaxial joint",
"page": "3911-3928"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10043-x",
"title": "Machine learning-based MRI radiomics identifies patients with degenerative cervical myelopathy and predicts baseline function",
"page": "5688-5697"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09762-y",
"title": "Development of a multimodal radiomics-based model for predicting recurrent fracture risk after PVP",
"page": "1419-1430"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09304-y",
"title": "Development of machine learning models for predicting patient-perceived benefit following lumbar disc herniation or spinal stenosis surgery",
"page": "591-605"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10181-2",
"title": "Automated scoliosis X-ray cobb angle classification: a deep learning approach with RadImageNet"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08895-w",
"title": "A fully automatic Cobb angle measurement framework of full-spine DR images based on deep learning",
"page": "3756-3766"
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{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-08089-2",
"title": "A neural network model for detection and classification of lumbar spinal stenosis on MRI",
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{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08623-w",
"title": "Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT",
"page": "1177-1186"
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{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07357-x",
"title": "Answer to the letter to the editor by Zhi-Hui Dai concerning “Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion” by Rudisill SS et al. (Eur Spine J [2022]; doi: 10.1007/s00586-022-07238-3)",
"page": "3161-3162"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08931-9",
"title": "Letter to the editor concerning “ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis” by Ahmed W, et al. (Eur spine J [2024]: doi:10.1007/s00586-024-08198-6)",
"page": "3056-3057"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08742-y",
"title": "Determining the risk factors for postoperative mechanical complication in degenerative scoliosis: a machine learning approach based on musculoskeletal metrics",
"page": "156-166"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09782-8",
"title": "Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning",
"page": "1127-1137"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-08084-7",
"title": "Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery",
"page": "4171-4181"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09782-8",
"title": "Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning",
"page": "1127-1137"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09794-4",
"title": "Using artificial intelligence and a walking aid to improve lumbar stability parameter evaluation methods",
"page": "1109-1116"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09869-2",
"title": "The “Spine Intelligence” Era: AI, simulations and LLMs advancing precision spine care",
"page": "1015-1016"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09168-2",
"title": "Development and validation of machine learning models to predict vertebral artery injury by C2 pedicle screws",
"page": "3950-3961"
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{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07892-1",
"title": "Developing a new tool for scoliosis screening in a tertiary specialistic setting using artificial intelligence: a retrospective study on 10,813 patients: 2023 SOSORT award winner",
"page": "3836-3845"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08948-0",
"title": "Development and validation of a radiomics model using plain radiographs to predict spine fractures with posterior wall injury",
"page": "3809-3819"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09520-6",
"title": "Extending convolutional neural networks to detect differences in symmetry in videorasterstereographic back scans with the aim to improve screening for adolescent idiopathic scoliosis",
"page": "414-421"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09939-5",
"title": "Development and validation of a deep learning radiomics model for predicting adjacent segment degeneration following anterior cervical discectomy and fusion",
"page": "5661-5674"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09481-w",
"title": "Letter to the Editor concerning “Development of machine learning models for predicting patient-perceived benefit following lumbar disc herniation or spinal stenosis surgery” by Z.A. Toh, et al. (Eur Spine J; doi:10.1007/s00586-025-09304-y)",
"page": "606-606"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07982-0",
"title": "Cross-sectional area and fat infiltration of the lumbar spine muscles in patients with back disorders: a deep learning-based big data analysis",
"page": "1-10"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-023-08099-0",
"title": "A deep neural network for MRI spinal inflammation in axial spondyloarthritis",
"page": "4125-4134"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08781-5",
"title": "Letter to the Editor concerning “Machine learning in predicting cauda equina imaging outcomes– a solution to the problem” by R. Sun et al. (Eur Spine J [2025]: doi.org/10.1007/s00586-024-08591-1)",
"page": "2036-2037"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08938-2",
"title": "Answer to the letter to the editor of X. Zhang, et al. concerning “AI versus the spinal surgeons in the management of controversial spinal surgery scenarios” by Mehmet, S. et al. (Eur spine J [2025]: doi.org/10.1007/s00586-025-08825-w)",
"page": "3054-3055"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09541-1",
"title": "Automated analysis of paraspinal muscles: segmentation and multi-parameter quantification in lumbar CT using convolutional neural network",
"page": "1249-1260"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09795-3",
"title": "Multi-criteria evaluation of clinical decision-making performance in spinal neurosurgery and physical therapy scenarios: A comparative analysis of artificial intelligence models",
"page": "1101-1108"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09102-6",
"title": "Development and validation of a prognostic prediction model for lumbar-disc herniation based on machine learning and fusion of clinical text data and radiomic features",
"page": "529-547"
},
{
"journal": "European Spine Journal",
"year": 2019,
"doi": "10.1007/s00586-019-05928-z",
"title": "Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods",
"page": "1433-1440"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09118-y",
"title": "Answer to the letter to the editor of T. Tu concerning “Machine learning-based models for outcome prediction in skull base and spinal chordomas: a systematic review and meta-analysis” by B. Hajikarimloo, et al. (Eur spine J [2025]: doi: 10.1007/s00586-025-09053-y)",
"page": "3633-3634"
},
{
"journal": "European Spine Journal",
"year": 2021,
"doi": "10.1007/s00586-021-06961-7",
"title": "Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion",
"page": "1952-1959"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09801-8",
"title": "Paraspinal muscle MRI radiomics outperform disc radiomics for predicting poor disability improvement after L4/5 single-level lumbar surgery",
"page": "1085-1100"
},
{
"journal": "European Spine Journal",
"year": 2020,
"doi": "10.1007/s00586-020-06406-7",
"title": "A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation",
"page": "2295-2305"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10180-3",
"title": "Letter to the Editor Re: “Deep learning models for lumbar spinal stenosis on MRI: model comparison and clinical benchmarking” by Lee et al. (Eur Spine J [2026] 35:548–558. DOI: 10.1007/s00586-025-09443-2)"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07307-7",
"title": "Performance of hybrid artificial intelligence in determining candidacy for lumbar stenosis surgery",
"page": "2149-2155"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09549-7",
"title": "A prediction model for postoperative respiratory failure in patients with traumatic cervical spinal cord injury using interpretable machine learning",
"page": "959-967"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08653-y",
"title": "Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database",
"page": "3659-3669"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07772-8",
"title": "Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury",
"page": "3825-3835"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10216-8",
"title": "Intelligent recognition and segmentation of anatomical structures in spinal endoscopy: a deep learning approach with 1000 annotated images"
},
{
"journal": "European Spine Journal",
"year": 2019,
"doi": "10.1007/s00586-019-06054-6",
"title": "Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial",
"page": "1580-1589"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08959-x",
"title": "MRI Radiomics based on paraspinal muscle for prediction postoperative outcomes in lumbar degenerative spondylolisthesis",
"page": "3820-3831"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08932-8",
"title": "Letter to the Editor concerning “AI versus the spinal surgeons in the management of controversial spinal surgery scenarios” by Mehmet, S. et al. (Eur spine J [2025]: doi.org/10.1007/s00586-025-08825-w)",
"page": "3052-3053"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09369-9",
"title": "Development and validation of machine learning models for predicting the risk of refracture after percutaneous kyphoplasty in OVCF patients",
"page": "968-977"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07923-x",
"title": "Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty",
"page": "3857-3867"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08588-w",
"title": "Temporal validation of the SORG 90-Day and 1-Year machine learning algorithms for survival of patients with spinal metastatic disease",
"page": "3649-3658"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07238-3",
"title": "Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion",
"page": "2104-2114"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08672-9",
"title": "Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods",
"page": "1146-1155"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09878-1",
"title": "Machine learning outperforms anthropometric scaling in predicting muscle parameters and spinal loading: a subject-specific musculoskeletal modeling study"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07354-0",
"title": "Letter to the Editor concerning “Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion” by Rudisill SS, et al. (Eur Spine J [2022]; doi: 10.1007/s00586-022-07238-3)",
"page": "3159-3160"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09032-3",
"title": "Prediction model of recurrence in patients with lumbar disc herniation after unilateral biportal endoscopy spinal surgery- XGBoost machine learning model can be interpreted based on SHAP",
"page": "3876-3887"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09753-z",
"title": "Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models",
"page": "1149-1161"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07320-w",
"title": "An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles",
"page": "2156-2164"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10069-1",
"title": "Performance of large language models in interpreting evidence-based clinical guidelines for lumbar disc herniation with radiculopathy"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-023-08109-1",
"title": "Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity",
"page": "4119-4124"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09347-1",
"title": "Letter to the Editor concerning “Evaluating ChatGPT-4.0’s accuracy and potential in idiopathic scoliosis conservative treatment: a preliminary study on clarity, validity, and expert perceptions” by negrini F, et al. (Eur Spine J [2025]: https://doi.org/10.1007/s00586-025-09166-4)",
"page": "5815-5816"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08708-0",
"title": "A deep learning-based prediction model for prognosis of cervical spine injury: a Japanese multicenter survey",
"page": "3714-3723"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10092-2",
"title": "Deep learning for predicting lumbar segmental instability using neutral lateral lumbar radiographs: a retrospective study"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08978-8",
"title": "Clinical-radiomics nomogram construction from magnetic resonance imaging to diagnose osteoporosis: a preliminary study",
"page": "3843-3852"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10130-z",
"title": "Scoping review: artificial intelligence in patient education for scoliosis"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07245-4",
"title": "A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation",
"page": "2115-2124"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08900-2",
"title": "Comparison of lumbar disc degeneration grading between deep learning model SpineNet and radiologist: a longitudinal study with a 14-year follow-up",
"page": "3767-3773"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10094-0",
"title": "Spine patient history-taking: a two phase study of a prompt-engineered large language model assistant"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07176-0",
"title": "Artificial intelligence in spine care: current applications and future utility",
"page": "2057-2081"
},
{
"journal": "European Spine Journal",
"year": 2021,
"doi": "10.1007/s00586-021-06866-5",
"title": "Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the “RAD” risk profile",
"page": "2167-2175"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10236-4",
"title": "Machine learning–based prediction of perioperative complications in spine surgery: a large-scale model development and validation study"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09813-4",
"title": "Development and comparison of machine learning models for predicting postoperative ileus after posterior thoracolumbar fracture surgery",
"page": "1401-1418"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09536-y",
"title": "Local fragility index and Radiomics-lite modeling of CT-Derived Hounsfield units for osteoporosis risk assessment in spine surgery patients",
"page": "1183-1193"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09320-y",
"title": "LASSO-based machine learning algorithm for prediction of dysphagia in patients suffering anterior cervical discectomy and fusion",
"page": "559-571"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09550-0",
"title": "AI in patient education: a comparative analysis of the quality and readability of AI generated patient education material on spinal fusion",
"page": "1221-1229"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09540-2",
"title": "Deep learning model for osteoporosis screening on chest CT with low tube voltage",
"page": "1230-1237"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-08035-2",
"title": "Letter to the editor concerning \"Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?\" by R. Stroop et al. (Eur Spine J [2023]; doi:10.1007/s00586-023-07975-z)",
"page": "361-361"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09446-z",
"title": "Letter to the Editor concerning “Age-based prediction of non-routine discharge after anterior cervical discectomy and fusion using machine learning” by G. Mastrokostas, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-09303-z)",
"page": "5857-5858"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09476-7",
"title": "Answer to the Letter to the Editor of S.D. Dedeepya, et al. concerning “Development of machine learning models for predicting patient-perceived benefit following lumbar disc herniation or spinal stenosis surgery” by Z.A. Toh, et al. (Eur Spine J; doi:10.1007/s00586-025-09304-y)",
"page": "5867-5868"
},
{
"journal": "European Spine Journal",
"year": 2022,
"doi": "10.1007/s00586-022-07155-5",
"title": "Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation",
"page": "2031-2045"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10141-w",
"title": "Deep learning based vertebra localization and Cobb angle estimation using x-ray images"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10348-x",
"title": "Large language models in the vertical integration of spine surgery workflow: a scoping review"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09892-3",
"title": "A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images",
"page": "5064-5081"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09017-2",
"title": "Letter to the editor concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by T.Shimizu, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2)",
"page": "3063-3064"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10126-9",
"title": "MRI-based radiomics reveals disc-level radiomic heterogeneity and is associated with symptom severity in single-level lumbar disc herniation"
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"journal": "European Spine Journal",
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"doi": "10.1007/s00586-025-09559-5",
"title": "Intelligent prediction of cervical instability based on MRI radiomics combined with clinical features",
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09053-y",
"title": "Machine learning-based models for outcome prediction in skull base and spinal chordomas: a systematic review and meta-analysis",
"page": "720-731"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-024-08636-5",
"title": "Correction: Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT",
"page": "1218-1218"
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{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08579-x",
"title": "CT-based radiomics predicts adjacent vertebral fracture after percutaneous vertebral augmentation",
"page": "528-536"
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{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08272-z",
"title": "Letter to the Editor concerning “ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis” by Ahmed W, et al. (Eur Spine J [2024]: doi:10.1007/s00586-024-08198-6)",
"page": "2919-2919"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08908-8",
"title": "Prediction of cervical spondylotic myelopathy from a plain radiograph using deep learning with convolutional neural networks",
"page": "3786-3797"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09447-y",
"title": "Answer to the Letter to the Editor of Tu et al. concerning “Age-based prediction of non-routine discharge after anterior cervical discectomy and fusion using machine learning” by Mastrokostas et al. (Eur Spine J [2025]; doi: 10.1007/s00586-025-09303-z)",
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08783-3",
"title": "Correction: Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods",
"page": "2040-2040"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09166-4",
"title": "Evaluating ChatGPT-4.0’s accuracy and potential in idiopathic scoliosis conservative treatment: a preliminary study on clarity, validity, and expert perceptions",
"page": "422-430"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09420-9",
"title": "Use of machine learning predictive models in sagittal alignment planning in adolescent idiopathic scoliosis surgery",
"page": "23-31"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09909-x",
"title": "AI model for automatic spinopelvic and spinal alignment parameters measurement from EOS"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09115-1",
"title": "Letter to the editor concerning “Machine learning-based models for outcome prediction in skull base and spinal chordomas: a systematic review and meta-analysis” by B. Hajikarimloo, et al. (Eur spine J [2025]: doi: 10.1007/s00586-025-09053-y)",
"page": "3631-3632"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09763-x",
"title": "Application and efficacy of artificial intelligence in patient education on spinal cord injuries"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10146-5",
"title": "ISSLS Prize in Bioengineering Science 2026: Hidden in Plain Sight: Machine Learning–Assisted MRI Reveals Novel Vertebral Body Biomarkers of Chronic Low Back Pain in Humans",
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},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09021-6",
"title": "Answer to the Letter to the Editor of W. Yue, et al. concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2)",
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},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09018-1",
"title": "Letter to the Editor concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2)",
"page": "3067-3068"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08659-6",
"title": "Machine learning insights into patient satisfaction following lateral lumbar interbody fusion",
"page": "3670-3682"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10049-5",
"title": "Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study"
},
{
"journal": "European Spine Journal",
"year": 2020,
"doi": "10.1007/s00586-020-06613-2",
"title": "Artificial intelligence facilitates decision-making in the treatment of lumbar disc herniations",
"page": "2176-2184"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10215-9",
"title": "Advancing spine connectomics and neural integration through machine learning and neuroengineering: a narrative review"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08282-x",
"title": "Answer to the Letter to the Editor of G. Shen, et al. concerning “ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis” by Ahmed W, et al. (Eur Spine J [2024]: doi:10.1007/s00586-024-08198-6)",
"page": "2920-2920"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08253-2",
"title": "Letter to the editor concerning “ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis” by Ahmed W, et al. (Eur Spine J [2024]: doi:10.1007/s00586-024-08198-6)",
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},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09489-2",
"title": "Answer to the letter to the Editor of T. Ruan concerning “Development of machine learning models for predicting patient-perceived benefit following lumbar disc herniation or spinal stenosis surgery” by Z.A. Toh, et al. (Eur Spine J; doi:10.1007/s00586-025-09304-y) by Bjørnar Berg and Zheng An Toh",
"page": "607-607"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08946-2",
"title": "Development of a deep learning model for measuring sagittal parameters on cervical spine X-ray",
"page": "3798-3808"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09443-2",
"title": "Deep learning models for lumbar spinal stenosis on MRI: model comparison and clinical benchmarking",
"page": "548-558"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09905-1",
"title": "AI-driven tissue discrimination sensing methods for robot-assisted spine surgery: a state-of-the-art review"
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{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07887-y",
"title": "MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation",
"page": "3892-3905"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09341-7",
"title": "Multimodal imaging fusion and machine learning model development: differential diagnosis of spinal inflammatory lesions using combined CT hounsfield units and MRI features",
"page": "1270-1282"
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{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08995-7",
"title": "Development and validation of a deep learning–powered system for multi-version global alignment and proportion score to predict mechanical complications after adult degenerative scoliosis surgery",
"page": "375-387"
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{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07963-3",
"title": "Predicting vertebral compression fracture prior to spinal SBRT using radiomics from planning CT",
"page": "3221-3229"
},
{
"journal": "European Spine Journal",
"year": 2021,
"doi": "10.1007/s00586-021-07036-3",
"title": "Use of machine learning to select texture features in investigating the effects of axial loading on T2-maps from magnetic resonance imaging of the lumbar discs",
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},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07562-2",
"title": "Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification",
"page": "3797-3806"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08939-1",
"title": "Answer to the letter to the editor of P.P. Ray concerning “ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis” by Ahmed W, et al. (Eur Spine J [2024]: doi:10.1007/s00586-024-08198-6)",
"page": "3058-3058"
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{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09785-5",
"title": "Cervical myelopathy screening using in-shoe inertial measurement unit sensors and machine learning focusing on gait disturbance",
"page": "1117-1126"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10280-0",
"title": "Are large language models such as ChatGPT, capable of supporting patients and general practitioners after spine surgery?"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10249-z",
"title": "Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07975-z",
"title": "Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?",
"page": "4135-4143"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-10204-y",
"title": "Large language models in spine care and research"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-08825-w",
"title": "AI versus the spinal surgeons in the management of controversial spinal surgery scenarios",
"page": "3736-3746"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09020-7",
"title": "Answer to the Letter to the Editor of H. Daungsupawong, et al. concerning “A multimodal machine learning model integrating clinical and MRI data for predicting neurological outcomes following surgical treatment for cervical spinal cord injury” by Shimizu T, et al. (Eur Spine J [2025]: doi: 10.1007/s00586-025-08873-2)",
"page": "3069-3069"
},
{
"journal": "European Spine Journal",
"year": 2023,
"doi": "10.1007/s00586-023-07535-5",
"title": "Correction to: Can artificial intelligence support or even replace physicians in measuring sagittal balance? A validation study on preoperative and postoperative full spine images of 170 patients",
"page": "1095-1097"
},
{
"journal": "European Spine Journal",
"year": 2021,
"doi": "10.1007/s00586-021-06916-y",
"title": "Morphological analysis of Kambin's triangle using 3D CT/MRI fusion imaging of lumbar nerve root created automatically with artificial intelligence",
"page": "2191-2199"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09548-8",
"title": "Machine learning-based prediction of pediatric scoliosis using foot pressure and body angles",
"page": "368-374"
},
{
"journal": "European Spine Journal",
"year": 2026,
"doi": "10.1007/s00586-026-09871-8",
"title": "Reliability and validity of adolescent idiopathic scoliosis diagnosis based on mobile phone AI scoliosis screening algorithm and infrared 3D spine assessment system"
},
{
"journal": "European Spine Journal",
"year": 2024,
"doi": "10.1007/s00586-024-08464-7",
"title": "A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification",
"page": "4204-4213"
},
{
"journal": "European Spine Journal",
"year": 2025,
"doi": "10.1007/s00586-025-09281-2",
"title": "Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists",
"page": "583-590"
},
{
"journal": "Acta Orthopaedica",
"year": 2017,
"doi": "10.1080/17453674.2017.1387732",
"title": "Will intelligent machine learning revolutionize orthopedic imaging?",
"page": "577-577"
},
{
"journal": "Acta Orthopaedica",
"year": 2020,
"doi": "10.1080/17453674.2020.1723292",
"title": "Deep learning in fracture detection: a narrative review",
"page": "362-362"
},
{
"journal": "Acta Orthopaedica",
"year": 2017,
"doi": "10.1080/17453674.2017.1344459",
"title": "Artificial intelligence for analyzing orthopedic trauma radiographs",
"page": "581-586"
},
{
"journal": "Acta Orthopaedica",
"year": 2025,
"doi": "10.2340/17453674.2025.44948",
"title": "Evaluation of migration analysis with AI-based CT-RSA and preoperative 3D-planning in total hip arthroplasty"
},
{
"journal": "Acta Orthopaedica",
"year": 2024,
"doi": "10.2340/17453674.2024.40182",
"title": "ChatGPT-4 generates orthopedic discharge documents faster than humans maintaining comparable quality: a pilot study of 6 cases",
"page": "152-156"
},
{
"journal": "Acta Orthopaedica",
"year": 2026,
"doi": "10.2340/17453674.2026.45733",
"title": "Response to Letter: Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients"
},
{
"journal": "Acta Orthopaedica",
"year": 2025,
"doi": "10.2340/17453674.2024.42702",
"title": "Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study"
},
{
"journal": "Acta Orthopaedica",
"year": 2022,
"doi": "10.2340/17453674.2021.843",
"title": "Machine learning classifiers do not improve prediction of hospitalization > 2 days after fast-track hip and knee arthroplasty compared with a classical statistical risk model",
"page": "117-123"
},
{
"journal": "Acta Orthopaedica",
"year": 2025,
"doi": "10.2340/17453674.2025.44248",
"title": "Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases",
"page": "521-528"
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{
"journal": "Acta Orthopaedica",
"year": 2021,
"doi": "10.1080/17453674.2021.1884408",
"title": "Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints",
"page": "254-257"
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{
"journal": "Acta Orthopaedica",
"year": 2026,
"doi": "10.2340/17453674.2026.45965",
"title": "Predicting persistent pain after total knee arthroplasty using different machine learning algorithms"
},
{
"journal": "Acta Orthopaedica",
"year": 2020,
"doi": "10.1080/17453674.2019.1711323",
"title": "Deep learning in fracture detection: a narrative review",
"page": "215-220"
},
{
"journal": "Acta Orthopaedica",
"year": 2021,
"doi": "10.1080/17453674.2021.1891512",
"title": "Deep neural networks with promising diagnostic accuracy for the classification of atypical femoral fractures",
"page": "394-400"
},
{
"journal": "Acta Orthopaedica",
"year": 2024,
"doi": "10.2340/17453674.2024.40905",
"title": "Artificial intelligence can be used in the identification and classification of shoulder osteoarthritis and avascular necrosis on plain radiographs: a training study of 7,139 radiograph sets",
"page": "319-324"
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{
"journal": "Acta Orthopaedica",
"year": 2019,
"doi": "10.1080/17453674.2019.1600125",
"title": "Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments",
"page": "394-400"
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{
"journal": "Acta Orthopaedica",
"year": 2026,
"doi": "10.2340/17453674.2026.45871",
"title": "External validation of machine learning models for estimation of mortality 1, 3, 6, and 12 months after hip fracture on 5,055 consecutive patients"
},
{
"journal": "Acta Orthopaedica",
"year": 2024,
"doi": "10.2340/17453674.2024.40949",
"title": "Development and validation of an artificial intelligence model for the classification of hip fractures using the AO-OTA framework",
"page": "340-347"
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{
"journal": "Acta Orthopaedica",
"year": 2020,
"doi": "10.1080/17453674.2020.1837420",
"title": "Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification",
"page": "102-108"
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{
"journal": "Acta Orthopaedica",
"year": 2019,
"doi": "10.1080/17453674.2019.1682850",
"title": "AI ethics, accountability, and sustainability: revisiting the Hippocratic oath",
"page": "1-2"
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{
"journal": "Acta Orthopaedica",
"year": 2026,
"doi": "10.2340/17453674.2026.45362",
"title": "Letter to the Editor: Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients",
"page": "240"
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{
"journal": "Acta Orthopaedica",
"year": 2018,
"doi": "10.1080/17453674.2018.1453714",
"title": "Automated detection and classification of the proximal humerus fracture by using deep learning algorithm",
"page": "468-473"
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{
"journal": "Acta Orthopaedica",
"year": 2020,
"doi": "10.1080/17453674.2020.1803664",
"title": "Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs",
"page": "699-704"
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