Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives

Area of Science:

• Cardiovascular Medicine
• Artificial Intelligence in Healthcare
• Pharmacotherapy Optimization

Background:

• Artificial intelligence (AI) demonstrates significant potential in advancing cardiovascular pharmacotherapy.
• AI can optimize drug selection, predict treatment efficacy, and forecast adverse effects to improve patient outcomes.
• Current applications leverage machine learning and in silico modeling for personalized cardiovascular care.

Purpose of the Study:

• To systematically review the state-of-the-art applications of AI in cardiovascular pharmacotherapy.
• To describe the potential of AI in guiding treatment decisions, refining clinical trial methodologies, and supporting drug discovery.
• To highlight the need for robust validation and standardized frameworks for AI integration in clinical practice.

Main Methods:

• Review of current literature on AI applications in cardiovascular pharmacotherapy.
• Analysis of machine learning and in silico modeling techniques used in drug selection and efficacy prediction.
• Examination of AI's role in clinical trial design, real-world data analysis, and drug discovery.

Main Results:

• AI can identify patient subgroups likely to benefit from specific treatments, expedite drug discovery, and reduce costs.
• Computational methods facilitate drug interaction detection and personalized interventions using real-world data.
• AI shows promise in streamlining clinical trials through real-time patient responsiveness data and enhanced recruitment.

Conclusions:

• AI offers substantial benefits for cardiovascular pharmacotherapy, including improved patient outcomes and efficient drug development.
• Robust validation across diverse populations and addressing data quality, privacy, and bias are critical for generalizability and equity.
• Standardized frameworks for data management, model certification, and transparency are essential for safe and effective AI integration into clinical practice.