ArticleAmerican journal of preventive cardiology2026
Inaccurate information regarding cardiovascular disease prevention enabled by generative artificial intelligence.
Article in American journal of preventive cardiology, 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
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
1 citing paper in PubMed.
- Artificial stupidity or logimorphism? How misuse of language warps our thinking about 'artificial intelligence'.European heart journal. Digital health · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inaccurate information regarding cardiovascular disease (CVD) prevention is prevalent on the internet and may influence medical decisions. Artificial intelligence "bots" are present on the internet and may be used for medical questions. This physician-led experiment evaluated the generation of inaccurate CVD information on two widely used generative artificial intelligence (genAI) models, namely OpenAI o1 and DeepSeek-R1. Performed in February 2025, this experiment was designed to evaluate genAI responses regarding nine commonly relevant CVD prevention topics, including statin therapy, supplements, and LDL cholesterol. Prompts were devised in two "tones", termed a neutral tone prompt and an inaccuracy tone prompt, the latter of which specifically requested inaccurate information. Two board-certified preventive cardiologists graded responses as appropriate, borderline, or inappropriate based on content and references. For the nine neutral tone prompts, 88.9 % (8/9) of OpenAI o1's responses and 66.7 % (6/9) of DeepSeek R1's were graded as appropriate. For the inaccuracy tone prompts, OpenAI o1 produced no appropriate responses (0/9), with 22.2 % (2/9) graded as borderline and 77.8 % (7/9) inappropriate. All of DeepSeek R1's replies (9/9) were graded as inappropriate. Findings highlight the relative ease with which genAI models can be prompted to produce inaccurate information on CVD prevention topics that are highly relevant to public health. Findings underscore the need for further research and policy interventions to mitigate AI-driven informational risks.
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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.