Artificial Intelligence Technologies in Cardiology.

Area of Science:

• Cardiology
• Artificial Intelligence
• Machine Learning

Background:

• The digital transformation of healthcare generates vast datasets, necessitating advanced analytical methods.
• Artificial intelligence (AI) shows significant promise in revolutionizing healthcare, building on successes in drug discovery and molecular chemistry.
• Machine learning (ML), a subset of AI, encompasses supervised, unsupervised, and reinforcement learning, offering diverse approaches to data analysis.

Purpose of the Study:

• To provide a comprehensive overview of the AI workflow, including common ML algorithms and performance metrics.
• To introduce explainable artificial intelligence (XAI) and its enabling technologies.
• To review AI implementations in cardiology, detailing algorithms used for different ML types and natural language processing.

Main Methods:

• Review of AI and ML methodologies, including supervised, unsupervised, and reinforcement learning.
• Explanation of performance metrics for regression and classification tasks.
• Exploration of explainable AI (XAI) concepts and technologies.
• Analysis of AI applications in cardiology, focusing on specific algorithms and natural language processing.

Main Results:

• AI and ML algorithms have demonstrated success in predicting pharmacological activities, reducing costs and time in scientific experiments.
• Various AI implementations in cardiology utilize supervised, unsupervised, and reinforcement learning, alongside natural language processing.
• Explainable AI (XAI) technologies are emerging to enhance the transparency and trustworthiness of AI models.

Conclusions:

• AI and ML offer transformative potential for healthcare, particularly in cardiology, by enabling sophisticated data analysis.
• The successful application of AI in medicine necessitates the establishment of clear legal, ethical, and methodical requirements for model deployment.
• Further research and development in AI, including XAI, are crucial for its responsible integration into clinical practice.