Abstract

Artificial intelligence (AI) in the field of orthopaedics has been a topic of increasing interest and opportunity in recent years. Its applications are widespread both for physicians and patients, including use in clinical decision-making, in the operating room, and in research. In this study, we aimed to assess the quality of ChatGPT answers when asked questions related to total knee arthroplasty (TKA).ChatGPT prompts were created by turning 15 of the American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines into questions. An online survey was created, which included screenshots of each prompt and answers to the 15 questions. Surgeons were asked to grade ChatGPT answers from 1 to 5 based on their characteristics: 1) Relevance; 2) Accuracy; 3) Clarity; 4) Completeness; 5) Evidence-based; and 6) Consistency. There were eleven Adult Joint Reconstruction fellowship-trained surgeons who completed the survey. Questions were subclassified based on the subject of the prompt: 1) risk factors, 2) implant/Intraoperative, and 3) pain/functional outcomes. The average and standard deviation for all answers, as well as for each subgroup, were calculated. Inter-rater reliability (IRR) was also calculated.All answer characteristics were graded as being above average (i.e., a score > 3). Relevance demonstrated the highest scores (4.43±0.77) by surgeons surveyed, and consistency demonstrated the lowest scores (3.54±1.10). ChatGPT prompts in the Risk Factors group demonstrated the best responses, while those in the Pain/Functional Outcome group demonstrated the lowest. The overall IRR was found to be 0.33 (poor reliability), with the highest IRR for relevance (0.43) and the lowest for evidence-based (0.28).ChatGPT can answer questions regarding well-established clinical guidelines in TKA with above-average accuracy but demonstrates variable reliability. This investigation is the first step in understanding large language model (LLM) AIs like ChatGPT and how well they perform in the field of arthroplasty.

View details for DOI 10.1016/j.arth.2024.02.023

View details for PubMedID 38364879