Towards explainability in artificial intelligence frameworks for heartcare: A comprehensive survey

Abstract

Artificial Intelligence is extensively applied in heartcare to analyze patient data, detect anomalies, and provide personalized treatment recommendations, ultimately improving diagnosis and patient outcomes. In a field where accountability is indispensable, the prime reason why medical practitioners are still reluctant to utilize AI models, is the reliability of these models. However, explainable AI (XAI) was a game changing discovery where the so-called back boxes can be interpreted using Explainability algorithms. The proposed conceptual model reviews the existing recent researches for AI in heartcare that have found success in the past few years. The various techniques explored range from clinical history analysis, medical imaging to the nonlinear dynamic theory of chaos to metabolomics with specific focus on machine learning, deep learning and Explainability. The model also comprehensively surveys the different modalities of datasets used in heart disease prediction focusing on how results differ based on the different datasets along with the publicly available datasets for experimentation. The review will be an eye opener for medical researchers to quickly identify the current progress and to identify the most reliable data and AI algorithm that is appropriate for a particular technology for heartcare along with the Explainability algorithm suitable for the specific task.

Keywords

• Explainability
• Electrocardiogram
• Metabolomics
• Electronic health record
• Data analytics
• Non-linear dynamics