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.

Editorial Commentary: Thoughtful Application of Artificial Intelligence Technique Improves Diagnostic Accuracy and Supportive Clinical Decision-Making.

Medical research within areas of deep learning, particularly in computer vision for medical imaging, has shown promise over the past decade with an increasing volume of technical papers published in orthopaedics related to imaging artificial intelligence (AI). However, as more tools and models are developed and deployed, it is easy for clinicians to get overwhelmed with the different types of models, leaving "artificial intelligence" as an empty buzzword where true value can be unclear. As with surgery, the techniques of deep learning require thoughtful application and cannot follow a one-size-fits-all approach as different problems require differential levels of technical complexity with model application.

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.

Enhancing Outcomes and Efficiency in Large Epidermal Cyst Management: Quality Improvement Approach in Primary Care.

Background: Epidermal cysts are common benign lesions encountered in primary care, especially in minor surgery clinics. The management of large epidermal cysts (>5 cm in diameter) poses significant challenges, including surgical intervention requirements, potential for complications, and impacts on patient care and clinic workflow. The prevalence of these cysts underlines the need for optimised management strategies that are essential for enhancing patient outcomes and clinic efficiency.

Determinants of Salary Variation and the Gender Pay Gap: A Survey of the American Association of Hip and Knee Surgeons (AAHKS) Surgeon Member Workforce.

Background: The increased emphasis on reimbursement, diversity, and burnout in hip and knee arthroplasty necessitates a foundational understanding of the surgeon workforce. The purpose of the study was to cross sectionally survey a representative sample of the AAHKS surgeon membership on the subject of salary, practice patterns, and demographic factors to establish a baseline framework for future advocacy efforts and initiatives.

Methods: An online survey was sent to AAHKS members between December 20, 2022 and January 19, 2023.

Custom Large Language Models Improve Accuracy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Models for Evidence-Based Medicine.

Purpose: The purpose of the study is to demonstrate the value of custom methods, namely Retrieval Augmented Generation (RAG)-based Large Language Models (LLMs) and Agentic Augmentation, over standard LLMs in delivering accurate information using an anterior cruciate ligament (ACL) injury case.

Methods: A set of 100 questions and answers based on the 2022 AAOS ACL guidelines were curated. Closed-source (open AI GPT4/GPT 3.

Patient-Specific Acetabular Safe Zones in Total Hip Arthroplasty: External Validation of a Quantitative Approach to Preoperatively Templating Spinopelvic Parameters.

Background: Spinopelvic mechanics are critical in total hip arthroplasty; however, there is no established consensus for adjusting acetabular component positioning based on spinopelvic parameters. This study aimed to (1) validate a recently developed Patient-Specific acetabular safe-zone calculator that factors in spinopelvic parameters and (2) compare differences with hip-spine classification targets.

Methods: A total of 3750 patients underwent primary total hip arthroplasty across 3 academic referral centers, with 33 (0.

A Glossary of Terms in Artificial Intelligence for Healthcare.

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal.

Editorial Commentary: Off-the-Shelf Large Language Models Are of Insufficient Quality to Provide Medical Treatment Recommendations, While Customization of Large Language Models Results in Quality Recommendations.

The content accuracy of off-the-shelf large language models (LLMs) mirrors the content accuracy of the unregulated Internet from which these generative artificial intelligence models are supplied. With error rates approximating 30% in terms of treatment recommendations for the management of common musculoskeletal conditions, seeking expert opinion remains paramount. However, custom LLMs represent an excellent opportunity to infuse niche, bespoke expertise from the many specialties and subspecialties within medicine.

Advanced technology in shoulder arthroplasty surgery: Artificial intelligence, extended reality, and robotics.

The purpose of this review is to provide an overview of the integration of technological advancements in orthopedic shoulder surgery. Recent technological advancements in orthopedic shoulder surgery include predictive analytics, computer-navigated instrumentation for operative planning, extended reality, and robotics. Separately, these advancements provide distinct methodological attempts to improve surgical experiences and outcomes.

Editorial Commentary: Large Language Models Like ChatGPT Show Promise, but Clinical Use of Artificial Intelligence Requires Physician Partnership.

Forcing ChatGPT and other large language models to perform roles reserved for physicians and other health care professionals-namely evaluation, management, and triage-poses a threat from regulatory, risk management, and professional perspectives. The clinical practice of medicine would benefit tremendously from automated administrative support with systems-based transparency and fluidity-not substitution for clinical diagnostics and decision making. ChatGPT and other large language models are not intended or authorized for clinical use, let alone to be tested or rubber stamped for this application.

The Practice Experience of an Adult Reconstruction Surgeon: A Cross-Sectional Analysis and Survey of the American Association of Hip and Knee Surgeons Membership.

Background: As demand for total hip arthroplasty and total knee arthroplasty increases, more surgeons have pursued subspecialty training in adult reconstruction. However, little information is available regarding the practice environment in which these fellowship-trained surgeons practice. The purpose of this study was to describe the practice environments of contemporary adult reconstruction surgeons.

Automating Linear and Angular Measurements for the Hip and Knee After Computed Tomography: Validation of a Three-Stage Deep Learning and Computer Vision-Based Pipeline for Pathoanatomic Assessment.

Background: Variability in the bony morphology of pathologic hips/knees is a challenge in automating preoperative computed tomography (CT) scan measurements. With the increasing prevalence of CT for advanced preoperative planning, processing this data represents a critical bottleneck in presurgical planning, research, and development. The purpose of this study was to demonstrate a reproducible and scalable methodology for analyzing CT-based anatomy to process hip and knee anatomy for perioperative planning and execution.

Computerized Versus Traditional Approaches for Total Knee Arthroplasty: A Quantitative Analysis of Knee Society Score and Western Ontario and McMaster Universities Osteoarthritis Index.

Total knee arthroplasty (TKA) is a common surgery for osteoarthritis, with increasing prevalence expected in the near future. This systematic review and meta-analysis compared the effectiveness of computerized TKA versus traditional TKA, focusing on postoperative outcomes measured by the Western Ontario and McMaster Universities osteoarthritis index (WOMAC) and the Knee Society score (KSS). A search on PubMed and Cochrane databases on November 14, 2023 for retrospective randomized controlled trials (RCTs) yielded data on WOMAC and KSS.

Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline.

Background: Dissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters.

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer.

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models.

Modern Hip Arthroscopy for FAIS May Delay the Natural History of Osteoarthritis in 25% of Patients: A 12-Year Follow-up Analysis.

Background: Little is known about the effect of modern hip arthroscopy on the natural history of femoroacetabular impingement syndrome (FAIS) with respect to joint preservation.

Purpose: To (1) characterize the natural history of FAIS and (2) understand the effect of modern hip arthroscopy by radiographically comparing the hips of patients who underwent only unilateral primary hip arthroscopy with a minimum follow-up of 10 years.

Study Design: Cohort study; Level of evidence, 3.

The Fundamentals and Applications of Wearable Sensor Devices in Sports Medicine: A Scoping Review.

Purpose: To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine.

Methods: A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature.

FEM wear prediction of ceramic hip replacement bearings under dynamic edge loading conditions.

Hard-on-Hard hip implants, specifically ceramic tribo-pair, have produced the highest in-vivo wear resistance, biocompatibility, superior corrosion resistance, and high fracture toughness. However, this ceramic tribo-pair suffers from edge loading, sharply increasing wear and accelerating early implant failures due to micro-separation. Even though in-vitro studies have tested the occurrence of wear due to dynamic edge loading, the Finite Element Method (FEM) gives the advantage of accurately estimating the wear, minimizing the experimental time and cost.