Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study
Open accessAbstract
Introduction: Artificial Intelligence (AI) chatbots, which generate human-like responses based on extensive data, are becoming important tools in healthcare by providing information on health conditions, treatments, and preventive measures, acting as virtual assistants. However, their performance in aligning with clinical practice guidelines (CPGs) for providing answers to complex clinical questions on lumbosacral radicular pain is still unclear. We aim to evaluate AI chatbots' performance against CPG recommendations for diagnosing and treating lumbosacral radicular pain. Methods: We performed a cross-sectional study to assess AI chatbots' responses against CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinical questions based on these CPGs were posed to the latest versions (updated in 2024) of six AI chatbots: ChatGPT-3.5, ChatGPT-4o, Microsoft Copilot, Google Gemini, Claude, and Perplexity. The chatbots' responses were evaluated for (a) consistency of text responses using Plagiarism Checker X, (b) intra- and inter-rater reliability using Fleiss' Kappa, and (c) match rate with CPGs. Statistical analyses were performed with STATA/MP 16.1. Results: We found high variability in the text consistency of AI chatbot responses (median range 26%–68%). Intra-rater reliability ranged from “almost perfect” to “substantial,” while inter-rater reliability varied from “almost perfect” to “moderate.” Perplexity had the highest match rate at 67%, followed by Google Gemini at 63%, and Microsoft Copilot at 44%. ChatGPT-3.5, ChatGPT-4o, and Claude showed the lowest performance, each with a 33% match rate. Conclusions: Despite the variability in internal consistency and good intra- and inter-rater reliability, the AI Chatbots' recommendations often did not align with CPGs recommendations for diagnosing and treating lumbosacral radicular pain. Clinicians and patients should exercise caution when relying on these AI models, since one to two-thirds of the recommendations provided may be inappropriate or misleading according to specific chatbots.
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Remaining confounding could not be fully managed due to past digital literacy, learning styles, and educator proficiency.
“Remaining confounding could not be fully managed, as a result of past digital literacy, learning styles and educator proficiency [29,30].”
From HOW AI TOOLS (LIKE CHATGPT/GEMINI) AFFECT CRITICAL THINKING IN NURSING STUDENTS. A SYSTEMATIC REVIEW (2026) · cites this paper as [30]
The Materials and methods > Procedure section describes steps to limit chatbot learning bias, while Discussion > Implications for clinical practice recommends digital health literacy training. Neither addresses remaining confounding from prior digital literacy, learning styles, or educator proficiency. Searches for those factors and for limitations did not find a passage supporting the claim; page numbers were not available in our copy. (LLM, conf=0.75)
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- Authors
- Rossettini, G., Bargeri, S., Cook, C., Guida, S., Palese, A., Rodeghiero, L., Pillastrini, P., Turolla, A., Castellini, G., & Gianola, S.
- Affiliations
- University of Verona (IT)
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- Duke University (US)
- University of Udine (IT)
- Krankenhaus Meran (IT)
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- Corresponding author
- Rodeghiero, Lia
- ORCID
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2025-06-27
- Volume
- 7
- Pages
- 1574287-1574287
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC12245906
- OpenAlex
- W4411706303
- Cited by
- 31
- References
- 38
- Citations by year
- 2026: 25 · 2025: 6
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion, other-oa)
- Institutional Research Information System (University of Udine) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministero della Salute
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- AI chatbots, lumbosacral radicular pain, guideline adherence, ChatGPT, Copilot, Gemini, Claude, perplexity, inter-rater reliability
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