High Rate of Return to Sport for Athletes Undergoing Articular Cartilage Restoration Procedures for the Knee: A Systematic Review of Contemporary Studies.

Background: Continued advancements in cartilage surgery and an accumulating body of evidence warrants a contemporary synthesis of return to sport (RTS) outcomes to provide updated prognostic data and to better understand treatment response.

Purpose: To perform an updated systematic review of RTS in athletes after knee cartilage restoration surgery.

Study Design: Systematic review; Level of evidence, 4.

Outcomes of Revision Cartilage Restoration Surgery for Failed Primary Treatment of Chondral or Osteochondral Defects of the Knee: A Systematic Review.

Background: Failure of primary cartilage restoration procedures of the knee that proceed to necessitating revision cartilage procedures represent a challenging clinical scenario with variable outcomes reported in previous literature.

Purpose: To perform a systematic review and meta-analysis of clinical outcomes and adverse events after revision cartilage restoration procedures of the knee for failed primary cartilage procedures.

Study Design: Systematic review and meta-analysis; Level of evidence, 4.

Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education.

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Processing capabilities have evolved from text only to being multimodal including text, images, audio, and video features. In health care settings, LLMs are being applied to several clinically important areas, including patient care and workflow efficiency, communications, hospital operations and data management, medical education, practice management, and health care research.

Ethical Application of Generative Artificial Intelligence in Medicine.

Generative artificial intelligence (AI) may revolutionize health care, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises ethical concerns related to data integrity, patient safety, and appropriate oversight. One of the primary ethical challenges lies in generative AI's potential to produce misleading or fabricated information, posing risks of misdiagnosis or inappropriate treatment recommendations, which underscore the necessity for robust physician oversight.

Return to Sport in Professional Athletes After Cartilage Restoration Surgery of the Knee: A Systematic Review and Meta-Analysis Demonstrates Gender Inequality and the Need for Improved Reporting.

Objective: To synthesize the literature concerning return to sport (RTS) and related outcomes after cartilage restoration surgery of the knee in professional athletes.

Design: Cochrane, PubMed, and OVID/Medline databases were queried for data pertaining to RTS after knee cartilage surgery in professional athletes. Demographic information, cartilage lesion characteristics, and RTS-specific information were extracted.

Applications of Artificial Intelligence for Health Care Providers.

Recent research shows that physicians lack the knowledge and ability to use artificial intelligence (AI) effectively. We thus introduce a new series of articles, "Applications of Artificial Intelligence for Health Care Providers." Like the arthroscope, AI is a powerful tool, and we must adapt our skills to effectively incorporate and apply this tool in our practices.

Favorable Outcomes Following Elbow Ulnar Collateral Ligament Reconstruction and Repair, Though Repair Associated with Increased Risk of Revision Surgery.

Purpose: To assess the risk of revision surgery following repair versus reconstruction of the medial ulnar collateral ligament (UCL) of the elbow in a national sample of patients in the United States.

Methods: This was a retrospective cohort study of young patients (≤35 years old) who underwent primary UCL reconstruction or repair for an isolated medial UCL injury of the elbow from October 2015 through October 2022 in a large national database (PearlDiver). Patient demographic data, comorbidities, surgical details, and concomitant ulnar nerve procedures were collected.

Diagnostic performance of deep learning for leg length measurements on radiographs in leg length discrepancy: A systematic review.

Purpose: To systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.

Methods: A systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.

Machine Learning Algorithms Can Be Reliably Leveraged to Identify Patients at High Risk of Prolonged Postoperative Opioid Use Following Orthopedic Surgery: A Systematic Review.

Machine learning (ML) has emerged as a method to determine patient-specific risk for prolonged postoperative opioid use after orthopedic procedures. : We sought to analyze the efficacy and validity of ML algorithms in identifying patients who are at high risk for prolonged opioid use following orthopedic procedures. : PubMed, EMBASE, and Web of Science Core Collection databases were queried for articles published prior to August 2021 for articles applying ML to predict prolonged postoperative opioid use following orthopedic surgeries.

Do Overall Weight, Body Mass Index, or Clinically Significant Weight Changes Occur After Total Joint Arthroplasty? A Meta-Analysis of 60,837 Patients.

Background: Total joint arthroplasty (TJA) is well-recognized for improving quality of life and functional outcomes of patients with osteoarthritis; however, TJA's impact on body weight remains unclear. Recent trends have demonstrated a shift among TJA patients, such that patients who have higher body mass indices (BMIs) are undergoing this common surgery. Given this trend, it is critical to characterize the impact TJA has on body weight or BMI.

A machine learning analysis of patient and imaging factors associated with achieving clinically substantial outcome improvements following total shoulder arthroplasty: Implications for selecting anatomic or reverse prostheses.

Background: Indications for reverse total shoulder arthroplasty(rTSA) continue to expand making it challenging to predict whether patients will benefit more from anatomic TSA(aTSA) or rTSA. The purpose of this study was to determine which factors differ between aTSA and rTSA patients that achieve meaningful outcomes and may influence surgical indication.

Methods: Random Forest dimensionality reduction was applied to reduce 23 features into a model optimizing substantial clinical benefit (SCB) prediction of the American Shoulder and Elbow Surgeon score using 1117 consecutive patients with 2-year follow up.

Editorial Commentary: The Scope of Medical Research Concerning ChatGPT Remains Limited by Lack of Originality.

There is no shortage of literature surrounding ChatGPT and whether this large language model can provide accurate and clinically relevant information in response to simulated patient queries. Unfortunately, there is a shortage of literature addressing important considerations beyond these experimental and entertaining uses. Indeed, a trend for redundancy has emerged where most of the literature has applied ChatGPT to the same tasks while simply swapping the subject matter, resulting in a failure to expand the impact and reach of this potentially transformational artificial intelligence (AI) solution.

Currently Available Large Language Models Do Not Provide Musculoskeletal Treatment Recommendations That Are Concordant With Evidence-Based Clinical Practice Guidelines.

Purpose: To determine whether several leading, commercially available large language models (LLMs) provide treatment recommendations concordant with evidence-based clinical practice guidelines (CPGs) developed by the American Academy of Orthopaedic Surgeons (AAOS).

Methods: All CPGs concerning the management of rotator cuff tears (n = 33) and anterior cruciate ligament injuries (n = 15) were extracted from the AAOS. Treatment recommendations from Chat-Generative Pretrained Transformer version 4 (ChatGPT-4), Gemini, Mistral-7B, and Claude-3 were graded by 2 blinded physicians as being concordant, discordant, or indeterminate (i.

Defining clinically relevant outcome thresholds for pain and function after osteochondral autograft transplantation of the knee.

Purpose: To define the minimal clinically important difference (MCID) for measures of pain and function at 2, 5 and 10 years after osteochondral autograft transplantations (OATs).

Methods: Patients undergoing OATs of the knee were identified from a prospectively maintained cartilage surgery registry. Baseline demographic, injury and surgical factors were collected.

Segond Fractures Can Be Identified With Excellent Accuracy Utilizing Deep Learning on Anteroposterior Knee Radiographs.

Purpose: To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts.

Methods: AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Using Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure.

Purpose: To assess the ability of ChatGPT-4, an automated Chatbot powered by artificial intelligence, to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance with Google Search Engine.

Methods: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top 10 frequently asked questions (FAQs) and associated sources were extracted.

The Large Language Model ChatGPT-4 Exhibits Excellent Triage Capabilities and Diagnostic Performance for Patients Presenting With Various Causes of Knee Pain.

Purpose: To provide a proof-of-concept analysis of the appropriateness and performance of ChatGPT-4 to triage, synthesize differential diagnoses, and generate treatment plans concerning common presentations of knee pain.

Methods: Twenty knee complaints warranting triage and expanded scenarios were input into ChatGPT-4, with memory cleared prior to each new input to mitigate bias. For the 10 triage complaints, ChatGPT-4 was asked to generate a differential diagnosis that was graded for accuracy and suitability in comparison to a differential created by 2 orthopaedic sports medicine physicians.

Clinical Results of Primary Repair Versus Reconstruction of the Anterior Cruciate Ligament: A Systematic Review and Meta-analysis of Contemporary Trials.

Background: Primary anterior cruciate ligament (ACL) repair has gained renewed interest in select centers for patients with proximal or midsubstance ACL tears. Therefore, it is important to reassess contemporary clinical outcomes of ACL repair to determine whether a clinical benefit exists over the gold standard of ACL reconstruction (ACLR).

Purpose: To (1) perform a meta-analysis of comparative trials to determine whether differences in clinical outcomes and adverse events exist between ACL repair versus ACLR and (2) synthesize the midterm outcomes of available trials.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.

Purpose Of Review: Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review.

Purpose: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts.

Methods: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded.

Hip Arthroscopy Simulator Training With Immersive Virtual Reality Has Similar Effectiveness to Nonimmersive Virtual Reality.

Purpose: To (1) compare the efficacy of immersive virtual reality (iVR) to nonimmersive virtual reality (non-iVR) training in hip arthroscopy on procedural and knowledge-based skills acquisition and (2) evaluate the relative cost of each platform.

Methods: Fourteen orthopaedic surgery residents were randomized to simulation training utilizing an iVR Hip Arthroscopy Simulator (n = 7; PrecisionOS) or non-iVR simulator (n = 7; ArthroS Hip VR; VirtaMed). After training, performance was assessed on a cadaver by 4 expert hip arthroscopists through arthroscopic video review of a diagnostic hip arthroscopy.

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review.

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications.

Relationships between throwing mechanics and shoulder anterior force in high school and professional baseball pitchers.

Background: Excessive shoulder anterior force has been implicated in pathology of the rotator cuff in little league and professional baseball pitchers; in particular, anterior laxity, posterior stiffness, and glenohumeral joint impingement. Distinctly characterized motions associated with excessive shoulder anterior force remain poorly understood.

Methods: High school and professional pitchers were instructed to throw fastballs while being evaluated with 3D motion capture (480 Hz).