The potential for large language models to transform cardiovascular medicine.

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiovascular diseases (CVDs) are a leading global cause of mortality.
• Early detection and prediction of CVDs remain significant clinical challenges.
• Traditional diagnostic methods have limitations in identifying subtle disease indicators.

Purpose of the Study:

• To explore the opportunities and limitations of AI models in cardiovascular medicine.
• To identify barriers and solutions for integrating AI into cardiovascular healthcare systems.
• To highlight AI's potential in early diagnosis, risk stratification, and patient support.

Main Methods:

• Review of recent advances in AI, including deep neural networks, transformer models, and multimodal AI.
• Integration of diverse data sources: electronic health records, medical images, genomics, and biosensors.
• Exploration of AI applications for both clinical decision support and direct patient assistance.

Main Results:

• AI, particularly deep neural networks, can significantly improve medical image interpretation accuracy.
• Multimodal AI facilitates the integration of heterogeneous data for enhanced diagnostic capabilities.
• AI holds promise for early arrhythmia detection and automated self-imaging, empowering patients.

Conclusions:

• AI presents substantial potential for revolutionizing cardiovascular medicine through early diagnosis and risk prediction.
• Addressing potential risks like data privacy and diagnostic errors is crucial for clinical adoption.
• Facilitating AI integration requires overcoming specific barriers to realize its full benefits in healthcare systems.