[Artificial intelligence for randomized controlled trials in cardiology: applications and future perspectives]

Area of Science:

• Cardiology
• Medical Informatics
• Clinical Trial Design

Background:

• Traditional cardiovascular trials face challenges including high costs, long durations, and the need for diverse populations.
• The increasing volume of data from clinical, imaging, and telemonitoring necessitates advanced analytical tools.
• Artificial intelligence (AI) offers potential solutions to streamline and improve cardiovascular clinical trial processes.

Purpose of the Study:

• To review the application of artificial intelligence (AI) in randomized controlled trials within cardiology.
• To highlight how AI can optimize patient selection, data collection, and outcome analysis.
• To discuss the potential of AI to address limitations in current cardiovascular trial designs.

Main Methods:

• Review of current literature on AI applications in cardiovascular clinical trials.
• Analysis of machine learning and deep learning algorithms used for data management and pattern identification.
• Exploration of AI's role in adaptive study protocols and endpoint accuracy.

Main Results:

• AI algorithms facilitate the management and analysis of large datasets, identifying predictive patterns and automating tasks.
• AI enables more adaptive study protocols, reduces interobserver variability, and improves endpoint accuracy.
• Emerging AI-driven approaches like digital biomarkers and synthetic control arms promise more efficient and targeted trials.

Conclusions:

• AI integration in cardiovascular trials can enhance efficiency, accuracy, and inclusivity.
• Addressing technical and ethical challenges, including algorithmic bias and privacy, is crucial for successful AI implementation.
• Future cardiovascular trials may be redefined by AI, leading to faster, more inclusive, and targeted experimental paradigms.