Explainable artificial intelligence (XAI) in medical decision support systems (MDSS): applicability, prospects, legal implications, and challenges

Abstract

The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult because of the issues with explainability. Explainable AI (XAI) has been studied as a possible remedy for the issues with current AI methods. The usage of machine learning (ML) with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS have played a crucial role in systems' attempts to advance patient wellbeing and the standard of care, particularly for non-communicable illnesses. Moreover, they have been a crucial prerequisite for the effective utilization of electronic healthcare (EHRs) data. This chapter bargains a comprehensive impression of the application of AI and XAI in MDSSs, summarizes recent research on the use and effects of MDSS in healthcare, and offers suggestions for users to keep in mind as these systems are integrated into healthcare systems and utilized outside of contexts for research and development.

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