ArticleRevista da Associacao Medica Brasileira (1992)2026
Artificial intelligence chatbots and idiopathic pulmonary fibrosis: are they ready to inform patients?
Article in Revista da Associacao Medica Brasileira (1992), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- The importance of response generation protocols in chatbot-based patient education studies.Revista da Associacao Medica Brasileira (1992) · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to determine the quality, reliability, and readability of the responses provided by artificial intelligence chatbots about idiopathic pulmonary fibrosis.
methodsThe clinically relevant questions about idiopathic pulmonary fibrosis diagnosis, treatment, prognosis, and lifestyle management were submitted to four widely used artificial intelligence chatbots, including ChatGPT, Perplexity, Gemini, and Copilot. Responses were assessed by five clinicians based on readability, understandability, quality, and content reliability using standardized tools.
resultsThe overall readability of chatbot-generated responses was low, corresponding to high educational requirements. Understandability ranged from moderate to good, whereas actionability remained moderate. Gemini produced the most readable and understandable outputs. Journal of the American Medical Association and Likert scores indicated limited source transparency but good guideline concordance. DISCERN analysis for the treatment question showed significant variation (p=0.003), with Perplexity achieving the highest total score.
conclusionAlthough artificial intelligence chatbots offer rapid and accessible information, their readability and source reliability remain limited. These findings highlight the necessity of expert supervision and further model improvement before artificial intelligence chatbots can be safely integrated into patient education.
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Registered trials
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