Artificial intelligence in cardiovascular medicine: clinical applications

Area of Science:

• Clinical Medicine
• Medical Informatics
• Artificial Intelligence

Background:

• Clinical decision-making relies on integrating diverse patient data (demographics, symptoms, labs, imaging).
• Personalized risk-benefit analysis is crucial for optimal patient management.
• Increasing data volume challenges traditional clinical workflows.

Purpose of the Study:

• To review the impact of the data revolution on clinical medicine.
• To highlight the role of artificial intelligence and machine learning (AI/ML) in modern healthcare.
• To discuss the potential benefits and necessary validation of AI/ML in clinical practice.

Main Methods:

• Review of current literature on data integration and AI/ML in clinical medicine.
• Analysis of AI/ML applications in patient data preparation, feature analysis, and risk assessment.
• Discussion of the importance of algorithm validation and re-evaluation.

Main Results:

• AI/ML can process and integrate extensive patient data, aiding clinicians.
• AI/ML facilitates comprehensive risk assessment for acute and chronic care.
• The integration of AI/ML represents a significant data revolution in medicine.

Conclusions:

• AI/ML offers substantial benefits for physicians and patients by enhancing clinical tasks.
• Proper assessment and validation are essential for the safe and effective clinical use of AI/ML.
• AI/ML is poised to radically transform clinical medicine with proper implementation.