AI-POWERED CARDIOVASCULAR HEALTH ANALYSIS: AN APPLICATION OF THE GPT-4O MODEL

• Dr. Amina T. Bello

  Department of Computer Engineering, University of Lagos, Nigeria

  Author

• Dr. Hassan R. Khalil

  Department of Biomedical Engineering, American University of Beirut, Lebanon

  Author

• Dr. Sarah M. Collins

  Department of Health Informatics, University of Sydney, Australia

  Author

Cardiovascular diseases (CVDs) remain a significant global health burden. Traditional diagnostic methods often face challenges in early detection and comprehensive risk assessment due to the multifactorial nature of these conditions. This article conceptually explores an innovative AI-powered cardiovascular health analysis system leveraging the advanced capabilities of the GPT-4o large language model. The proposed system integrates diverse patient data, including structured clinical records, unstructured clinical notes, and potentially multimodal inputs, to provide enhanced diagnostic accuracy, personalized risk stratification, and streamlined clinical workflows. By utilizing GPT-4o's sophisticated transformer architecture and contextual understanding, the system aims to identify subtle patterns and correlations that can improve early detection and intervention. While promising, the implementation faces challenges such as data bias, hallucination risks, ethical considerations, and the imperative for human oversight. Future directions include full multimodal data integration, continuous learning mechanisms, and robust explainable AI. This initiative seeks to transform cardiovascular health analysis by integrating cutting-edge AI for more precise and proactive patient care.

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