Evaluation of ChatGPT-4o as a Patient Information Tool for Common Orthopaedic Surgeries: Accuracy, Completeness, and Clinical Utility
Bibliographic record
Abstract
INTRODUCTION: Artificial intelligence chatbots, such as ChatGPT-4o ("omni"), a large language model developed by OpenAI that integrates text, image, and audio processing with web connectivity, have gained traction as potential patient education tools in orthopaedic surgery. This study aimed to evaluate the accuracy, completeness, and clinical utility of ChatGPT-4o's responses to common patient questions about six widely performed orthopaedic procedures. METHODS: We assessed ChatGPT-4o's responses to five standardized patient-oriented queries for total knee arthroplasty, total hip arthroplasty, anterior cruciate ligament reconstruction, rotator cuff repair, anterior cervical diskectomy and fusion, and carpal tunnel release. Responses were generated using ChatGPT-4o's web-enabled version in January 2025. Two resident orthopaedic surgeons independently rated each response for accuracy, completeness, layperson clarity, misleading content, and conciseness using a structured binary rubric. The validated DISCERN instrument (16 items, max score 80) was adapted for quantitative assessment of information quality. Interrater reliability was assessed with Cohen kappa. RESULTS: Overall, ChatGPT-4o generated accurate and structured responses, free of overt errors. The average DISCERN score across procedures was 43.5, classifying the information as fair. The highest average DISCERN score was for anterior cervical diskectomy and fusion (mean 45.8 ± 10.1), whereas the lowest was for rotator cuff repair (mean 41.6 ± 5.9). Factual accuracy was high (>90%), but 36% of responses contained some misleading or incomplete information. Responses explaining treatment alternatives were the most accurate and complete, whereas those outlining surgical risks performed worst. Interrater agreement was good (Cohen kappa = 0.64). DISCUSSION: ChatGPT-4o provided generally accurate, clear, and empathetic explanations of common orthopaedic surgeries, offering a promising adjunct to conventional patient education. However, key limitations particularly regarding alternative treatments, nuanced risks, and lack of tailored advice limit its stand-alone use in clinical practice. Careful oversight and clinician vetting remain essential. CONCLUSIONS: ChatGPT-4o can supplement orthopaedic patient education by offering accessible, engaging content. However, notablenotable gaps in detail and occasional misleading information necessitate careful review and contextual explanation by orthopaedic surgeons.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".