OP1.5 Evaluating ChatGPT’s Performance in Answering Patients’ Questions Relating to Femoroacetabular Impingement Syndrome and Arthroscopic Hip Surgery
Bibliographic record
Abstract
Abstract Background: This study evaluates the efficacy of large language models (LLMs) like ChatGPT in providing accurate and reliable patient information on Femoroacetabular Impingement (FAI) syndrome and its arthroscopic management. The advent of AI and LLMs has revolutionized the accessibility of medical information, necessitating an examination of their reliability and accuracy. With the known preponderant reliance on online resources for medical information, this research aims to assess the precision of ChatGPT responses to common patient inquiries about FAI and its surgical treatment. Hence, this project’s primary goal was to ascertain the overall accuracy and reliability of ChatGPT-generated information, with a secondary aim of comparing the performance between ChatGPT versions 3.5 and 4.0. Methods: Utilizing a set of twelve frequently asked questions about FAI, collected from scientific literature and reputable healthcare websites, the study evaluated and compared responses from ChatGPT versions 3.5 and 4.0. These responses were evaluated in a blinded fashion by three experienced hip arthroscopy surgeons using a previously published ChatGPT Response Rating System, ranging from “excellent response not requiring clarification” to “unsatisfactory requiring substantial clarification.” A descriptive quantitative and qualitative analysis was conducted. A Wilcoxon signed-rank test was used to compare the paired groups (GPT 3.5 versus GPT 4.0) and Gwet’s AC2 coefficient was used to assess the weighted level of agreement, corrected for chance, employing quadratic weights. Results: The findings indicated that both ChatGPT versions predominantly produced responses that were either “excellent” or “satisfactory requiring minimal clarification”, representing 75% and 92% of the responses for ChatGPT 3.5 and 4.0 respectively. The median accuracy scores were 2 (range 1-3) and 1.5 (range 1-3) for ChatGPT 3.5 and ChatGPT 4.0, respectively. No response was judged “unsafe or requiring substantial clarification” by the experts. However, no significant statistical difference was found between the two versions (p=0.279), although ChatGPT-4 showed a tendency towards higher accuracy in some areas. Conclusion: ChatGPT demonstrates a promising capacity to provide accurate and helpful information on FAI syndrome and its treatment, with both versions performing to satisfaction. This research underscores the importance of ongoing evaluation and refinement of AI tools in healthcare, ensuring their reliability and effectiveness in patient education and support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".