ArticleJournal of medical Internet research2024
Gender Bias in AI's Perception of Cardiovascular Risk.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts.Journal of general internal medicine · 2026Article
- Process Improvement Before Artificial Intelligence and Automation: Building Trust With the Understand-Transform-Sustain Framework.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- An AI-enhanced NHS will rely on clinical guidelines, but what if the evidence is inequitable?BMJ public health · 2026Article
- Advances in biomonitoring technologies for women's health.Nature communications · 2025Review
- Large Language Models in Nursing Education: Concept Analysis.JMIR nursing · 2025Review
- Promises and perils of generative artificial intelligence: a narrative review informing its ethical and practical applications in clinical exercise physiology.BMC sports science, medicine & rehabilitation · 2025Review
- Uncovering bias and variability in how large language models attribute cardiovascular risk.Frontiers in digital health · 2025Article
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
The study investigated gender bias in GPT-4's assessment of coronary artery disease risk by presenting identical clinical vignettes of men and women with and without psychiatric comorbidities. Results suggest that psychiatric conditions may influence GPT-4's coronary artery disease risk assessment among men and women.
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.