Expert Assessment of ChatGPT’s Answers to Patient Questions About Standard and Fast-Track Total Knee Arthroplasty
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By
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Bartosz Maciag
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Dawid Jegierski
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Andrzej Frankiewicz
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Piotr Tarkowski
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Krystian Żarnovsky
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Filip Mietlicki
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Artur Stolarczyk
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October 15, 2026
Clinical Report: Expert Assessment of ChatGPT’s Answers to Patient Questions About Standard and Fast-Track Total Knee Arthroplasty
Overview
The study was designed to assess expert ratings of ChatGPT-generated answers to patient questions about conventional and fast-track total knee arthroplasty (TKA). The supplied material describes the study rationale and methods, but does not provide response ratings or results; therefore, no conclusions about answer quality or differences between question groups can be reported.
Background
TKA is an effective treatment for end-stage knee osteoarthritis, and contemporary perioperative care increasingly uses Enhanced Recovery After Surgery and fast-track pathways. These pathways may involve early mobilization and discharge, multimodal analgesia, and selective rather than routine use of drains and urinary catheters. Patient education is important because shorter hospital stays place greater emphasis on patients’ understanding of recovery, rehabilitation, warning symptoms, pain management, and wound care. ChatGPT and other large language models are increasingly used for health information, while prior orthopaedic investigations described in the source material report generally accurate and readable responses alongside concerns about completeness and the need for professional oversight.
Data Highlights
| Study element | Details reported |
|---|---|
| Design | Cross-sectional expert-evaluation study; no patient-level data or clinical interventions were used. |
| Initial question set | 91 patient questions about TKA drawn from outpatient consultations and publicly accessible educational materials. |
| Conventional TKA questions | 50 highest-scoring questions selected using a modified Delphi process and ratings from five panel members: three arthroplasty surgeons, a physiotherapist, and an orthopaedic nurse. |
| Fast-track TKA questions | 20 questions formulated by clinicians experienced in fast-track TKA. |
| Rating criteria | Frequency in clinical practice, clinical relevance, and patient-safety significance; each criterion used a 5-point Likert scale. |
| Results available in supplied material | Response ratings and study findings are not included. |
Key Findings
- The study’s primary aim was to characterize expert assessments of ChatGPT’s explanations of fast-track TKA concepts.
- Ratings of answers to conventional TKA questions were also planned, with group differences explored; the authors state that the differently constructed question sets were not intended to test equivalence or noninferiority.
- The conventional question bank began with 91 questions and was narrowed to 50 through a modified Delphi process involving five clinical professionals.
- The 20 fast-track questions covered topics including early mobilization and discharge, multimodal analgesia, selective drainage and catheter use, self-management, thromboprophylaxis, and post-discharge safety.
- The background describes earlier orthopaedic LLM publications as finding responses that may be generally accurate and readable, while noting incomplete content, variable comprehensiveness, and the need for professional oversight.
Clinical Implications
The supplied material identifies patient education about recovery, mobilization, pain and wound care, and post-discharge safety as important components of fast-track TKA. It also notes that prior orthopaedic LLM research has identified a need for professional oversight; the present study’s results are not provided, so its assessment of ChatGPT’s answers cannot be translated into further clinical conclusions.
Conclusion
The source describes a structured expert-evaluation study of ChatGPT answers to conventional and fast-track TKA questions, but does not include its results. Accordingly, the quality of the evaluated answers and any differences between the question groups remain unreported in the supplied material.
Related Resources & Content
- ERAS Society, PMC, 2021 -- Consensus statement for perioperative care in total hip replacement and total knee replacement surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations
- Knee Surgery, Sports Traumatology, Arthroscopy, 2023 -- Investigating the Role of ChatGPT as an Adjunct Resource for Orthopaedic Information
- Obesity Surgery, 2023 -- Evaluating the Precision of ChatGPT's Responses to Inquiries About Bariatric Surgery
- Knee Surgery, Sports Traumatology, Arthroscopy — Achieving a Computationally Simulated Optimal Alignment During TKA Reduces Patient-Reported Functional Impairment
- The Journal of Clinical Endocrinology & Metabolism — Evaluating Responses to Thyroid-Related Inquiries: A Comparison Between ChatGPT and Surgeons
- Consensus statement for perioperative care in total hip replacement and total knee replacement surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations
- Reducing the length of hospital stay for patients undergoing primary total knee arthroplasty by application of enhanced recovery after surgery (ERAS) pathway: a multicenter, prospective, randomized controlled trial
- Clinical Outcomes and Complication Rates in Fast-Track Versus Routine after Total Knee Arthroplasty: A Systematic Review and Meta-analysis
- Consensus statement for perioperative care in total hip replacement and total knee replacement surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations - PMC
- AAOS Updates Clinical Practice Guideline to Prevent Total Hip and Knee Arthroplasty Periprosthetic Joint Infection in Patients Undergoing Dental Procedures
Based on findings from:
Expert evaluation of ChatGPT-generated responses to patient questions in conventional and fast-track total knee arthroplasty
Bartosz Maciag, Dawid Jegierski, Andrzej Frankiewicz, Piotr Tarkowski, Krystian Żarnovsky, Filip Mietlicki, Artur Stolarczyk. Archives Of Orthopaedic And Trauma Surgery, 2026.
https://link.springer.com/article/10.1007/s00402-026-06534-7
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.