ArticleArchives of rheumatology2025
Assessment of the Artificial Intelligence- Generated Fibromyalgia Information: Beyond the Hype.
Article in Archives of rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Cross-platform evaluation of LLM-generated educational texts on cardiac myxoma: quality, readability, and actionability using network analysis and latent profile analysis.Frontiers in cardiovascular medicine · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/Aims: Individuals increasingly turn to artificial intelligence (AI) chatbots for health-related information; however, the accuracy and usability of their responses remain uncertain. This study assessed the quality, comprehensiveness, and readability of responses from 6 AI chatbots-ChatGPT-3.5, ChatGPT-4o (OpenAI), Copilot AI (Microsoft), Perplexity AI (Perplexity.AI), Gemini AI (Google), and ChatSonic AI (Writesonic)-to the most commonly searched fibromyalgia-related queries. Materials and Methods: The top 10 most frequently searched fibromyalgia-related questions from the past 2 years were retrieved from the Google Trends database. Each chatbot was queried separately, and a total of 60 responses (10 per chatbot) were assessed both qualitatively and quantitatively by 2 reviewers, focusing on content quality, accuracy, readability, and alignment with evidence-based guidelines. Results: ChatGPT-3.5 had the lowest Ensuring Quality Information for Patients score (20.6 ± 4.5), indicating very low quality information, while Gemini achieved the highest (40.5 ± 5), which was still classified as low quality. Understandability was moderate for Copilot, Gemini, and Perplexity (67.2) but lowest for ChatGPT-3.5 (43.2 ± 10.2). Actionability was weak and the misinformation assessment revealed a moderate level across all chatbots. Readability scores indicated university-level complexity, with ChatGPT-4o having the lowest Reading Ease score (11.3 ± 11.2) and Copilot the highest (30.3 ± 13.2). Conclusion: While AI chatbots provide accessible health information, their accuracy and depth vary. Gemini, Copilot, and Perplexity AI showed better quality, but citation inconsistencies, readability challenges, and misinformation risks highlight the need for refinement beyond the hype. Clinicians should guide fibromyalgia patients in critically assessing AI-generated health content. Future research should explore improvements in AI chatbot applicability for medical inquiries.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.