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.

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.

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.