Introduction: Patients with musculoskeletal complaints often search online to identify an appropriate healthcare provider. With the increasing availability of large language models (LLMs), these artificial intelligence (AI) tools can direct patients to providers. This study evaluated the ability of LLMs to recommend appropriate providers based on representative patient musculoskeletal queries. Methods: Three LLMs (ChatGPT, DeepSeek, and Gemini) were prompted with standardized musculoskeletal queries for two US cities (Lynchburg, VA, and Trumbull, CT). Provider recommendations were considered appropriate if the physician was currently practicing in the requested location and specialized in the relevant area. Listed phone numbers were checked for accuracy. Descriptive statistics and Fisher exact tests were used to summarize findings. Results: The appropriateness of recommended providers differed across models with ChatGPT being most often appropriate (17/17, 100%) compared with Gemini (9/21, 43%) and DeepSeek (4/10, 40%), (P < 0.001). Of the 18 inappropriate recommendations, 13 (72%) were real providers in unrelated specialties and 5 (28%) were hallucinations, all from DeepSeek. Phone number accuracy differed significantly across models with Gemini being most accurate (5/6, 83%), outperforming both ChatGPT (6/9, 67%; P = 0.60) and DeepSeek (2/10, 20%; P = 0.04). Discussion: LLMs showed potential to direct patients to local, specialized musculoskeletal providers based on their report, although the specific contact information was at times inaccurate. As these tools evolve, providers should be aware of AI's ability to make provider recommendations and work to ensure the presentation of their contact information is accessible by these models as best possible.
Ethan C. Gazan, Colin M. Emrich, Alexander J. Baur et al.· Journal of the American Acad...· 0 citations
Background The Forgotten Joint Score (FJS) is a patient-reported outcome (PRO) measure assessing joint awareness after total knee arthroplasty (TKA). Unlike traditional PROs, it is particularly sensitive to the “forgotten” quality of the joint, correlating with patient satisfaction and long-term success. This systematic review evaluates the FJS compared to conventional PROs in assessing TKA outcomes. Methods A systematic search of PubMed, Embase, and the Cochrane Library identified randomized controlled trials reporting FJS alongside other PROs in primary TKA. Studies included various alignment techniques, implants, and robotic assistance. Independent reviewers performed data extraction and risk of bias assessments. Pooled FJS scores were analyzed using random-effects models, with subgroup analyses by surgical technique and prosthesis type. Results Fourteen randomized controlled trials (1244 knees) met inclusion criteria. FJS scores ranged from 19.00 to 98.00, with a mean of 73.61. Unlike Western Ontario and McMaster Universities, Oxford Knee Score, and Knee Society Score, the FJS showed no significant ceiling effect. Chi-square tests demonstrated that FJS detected significant postoperative improvements compared to Western Ontario and McMaster Universities and Knee Society Score (P = .011 and P = .037). Subgroup analyses found no significant differences in FJS or other PROs, though recent studies suggest a trend favoring kinematic alignment. Conclusions The FJS demonstrates superior sensitivity and avoids ceiling effects compared to traditional PROs, making it a valuable tool for evaluating subtle differences in TKA outcomes. Its ability to detect nuanced improvements highlights its clinical relevance in optimizing surgical techniques and enhancing patient-centered care. Future research should prioritize the FJS to refine alignment strategies and implant designs.
Anand Dhaliwal, Alexander J. Baur, Brendan J. Liakos et al.· Arthroplasty Today· 0 citations
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