Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.
iScience
; 27(2): 109022, 2024 Feb 16.
Article
in En
| MEDLINE
| ID: mdl-38357664
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Clinical_trials
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
IScience
Year:
2024
Document type:
Article