ArticleCureus2025
Assessing the Suitability of ChatGPT and DeepSeek AI for Patient Education on Common Rheumatological Disorders.
Article in Cureus, 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 of DeepSeek in Medicine: Bibliometric Analysis and Scoping Review.Journal of medical Internet research · 2026Article
- Development of an AI chatbot to support shared decision-making in rheumatoid arthritis: a two-phase usability study.BMC medical informatics and decision making · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Systemic lupus erythematosus (SLE), systemic sclerosis, and dermatomyositis are among the most prevalent rheumatological conditions. Using artificial intelligence tools (AI) like ChatGPT and Deepseek AI in health care can help in providing personalized patient education, resulting in improved health care literacy and treatment adherence. Aim To assess and compare ChatGPT and DeepSeek AI's effectiveness in generating understandable, accurate, and reliable patient education guide for three rheumatological conditions: systemic lupus erythematosus, systemic sclerosis, and dermatomyositis. Methodology ChatGPT 4.0 and DeepSeek AI were asked to write a patient education guide for "systemic lupus erythematosus", "systemic sclerosis," and " dermatomyositis". These materials were assessed using validated readability scores (Flesch-Kincaid, grade level, ease score), linguistic complexity analysis (average syllables per word, words per sentence), and similarity metrics to standard rheumatological resources. Finally, the reliability was rated using " discern score, a structured evaluation framework formed based on evidence-based guidelines from the British Society of Rheumatology and the American College of Rheumatology. Results There was no significant difference in the word count (p=0.775), sentence count (p=0.802), average word per sentence (p=0.349), average syllables per sentence(p=0.101), grade level (p=0.193), similarity% (p=0.481), reliability score (p=0.742), and ease score (p=0.097) between ChatGPT and Deepseek AI. Conclusions Both ChatGPT and DeepSeek AI offer promising avenues for augmenting patient education in rheumatology, but due to the limitations, their output should be used as a complement - not a replacement - for verified, expert-reviewed educational materials.
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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.