ArticleFrontiers in digital health2026
Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large language models (LLMs) are increasingly used by patients seeking cardiovascular health information through digital platforms. However, their accuracy and suitability for providing guidance on heterogeneous diseases such as cardiomyopathies and heart failure remain inadequately evaluated. This study systematically benchmarked state-of-the-art LLMs on patient-oriented heart failure and cardiomyopathy queries regarding clinical appropriateness and comprehensibility. Methods: Six prominent LLM Chatbots were tested on 50 expert-curated questions covering disease understanding and lifestyle advice. A web-based evaluation platform randomized and blinded responses for assessment by twelve reviewers (cardiologists, medical students, AI auto-graders). Responses were rated on a 1-5 Likert scale across nine domains, including appropriateness, readability, and empathy. Reviewers also chose their preferred model per question. Results: Linguistic complexity and output length varied substantially. Gemini provided the most readable responses (Flesch-Kincaid Grade 11.3 ± 1.9) but was among the most verbose (668.7 ± 116.1 words). Across 2,700 ratings, Gemini received the highest composite mean rating (4.41 ± 0.77), excelling in completeness and factual reliability, followed by Grok (4.23 ± 0.76). Confabulation avoidance scored consistently high across all models (4.49 ± 0.02), while conciseness scored lowest (3.81 ± 0.05). Consistently, evaluators selected Gemini as their preferred information source in 43.7%, followed by Grok (30.3%). Rating tendencies varied by evaluator group: Auto-graders gave the highest average scores (mean 4.58 ± 0.60), followed by students (4.10 ± 0.88), while experts were more conservative (3.79 ± 0.93). Discussion: All LLMs showed good accuracy avoiding medical misinformation, though variability exists in readability and comprehensiveness. While major factual errors or hallucinations were rare in our blinded evaluation, they were not entirely absent.
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.