ArticleiScience2024
Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Large Language Models in Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Natural Language Processing for Risk Stratification of Emergency Department Patients with Chest Pain.CJC open · 2026Article
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node Metastasis Prediction: Development of a Knowledge-Augmented Framework.JMIR medical informatics · 2026Article
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.Communications medicine · 2026Article
- Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome.iScience · 2026Article
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- GastroTCM: a large language model assistant for gastroenterology in traditional Chinese medicine.Chinese medicine · 2026Article
- Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM): 2025 Updates.Korean journal of radiology · 2025Review
- AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning.Nature communications · 2025Article
- Transforming Cardio-Oncology Care Through AI-Driven Large Language Model Systems: A Roadmap for Future Implementation.JACC. Advances · 2025Article
- Confidence-linked and uncertainty-based staged framework for phenotype validation using large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial Intelligence Large Language Models in Cardiology.Reviews in cardiovascular medicine · 2025Article
- Areas of research focus and trends in the research on the application of AIGC in healthcare.Journal of health, population, and nutrition · 2025Article
- Performance of single-agent and multi-agent language models in Spanish language medical competency exams.BMC medical education · 2025Article
- Charting the future of cardiology with large language model artificial intelligence.Nature reviews. Cardiology · 2025Article
- Clinical Sentiment Analysis by Large Language Models Enhances Prediction of Hepatorenal Syndrome.Gastro hep advances · 2025Article
- Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.
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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.