ArticleClinical Medicine Insights. Cardiology2025
Reliability and Readability Assessment of Atrial Fibrillation Patient Information Delivered by Artificial Intelligence-Based Language Models (ChatGPT, YouChat, Gemini, and Perplexity AI) in English and Spanish.
Article in Clinical Medicine Insights. Cardiology, 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.
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Reliability and readability of Artificial Intelligence generated information on acute cholecystitis: A comparative analysis.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Atrial fibrillation (AF) is the most prevalent arrhythmia and a significant cause of morbidity. Artificial intelligence (AI)-based language models represent a novel tool for searching for medical information; however, there is still uncertainty regarding their reliability and readability in different languages. Objective: To assess the reliability and readability of information provided by AI-based models for patients with AF. Methods: A cross-sectional study was conducted to assess the reliability and readability of the responses generated by ChatGPT, YouChat, Gemini and Perplexity on AF in English and Spanish. Thirty standardised questions were posed in both languages. The quality of the responses was then assessed by 2 independent reviewers via a standardised tool. Readability was assessed via the Flesch-Szigrist formula. The results were then compared by tool and language. Results: ChatGPT demonstrated the highest interrater agreement (PA = 0.73 in Spanish, 0.80 in English), followed by Gemini in English (PA = 0.66). In Spanish, ChatGPT generated the highest percentage of complete responses (80%), followed by Perplexity (73%) and Gemini (47%). In English, Perplexity demonstrated the strongest performance, with a score of 93%, followed by ChatGPT, with 73%, and Gemini, with 53%. A readability analysis revealed significant differences between the models ( Conclusion: ChatGPT and Perplexity emerged as the most reliable models, although readability remains a concern. There is a clear need for improvements to optimise the accuracy and accessibility of AI-generated medical information.
Indexed as
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.