Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Institute of Engineering Technology (IET)
Abstract
The healthcare sector is very interested in machine learning (ML) and artificial
intelligence (AI). Nevertheless, applying AI applications in scientific contexts is
difficult due to explainability issues. Explainable AI (XAI) has been studied as a
potential remedy for the problems with current AI methods. The usage of 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 has played a crucial role in systems’ attempts
to improve patient safety and the standard of care, particularly for noncommunicable
illnesses. They have moreover been a crucial prerequisite for
effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad
overview of the application of XAI in MDSS toward various infectious diseases,
summarizes recent research on the use and effects of MDSS in healthcare with
regard to non-communicable diseases, and offers suggestions for users to keep in
mind as these systems are incorporated into healthcare systems and utilized outside
of contexts for research and development.
Description
Keywords
Explainable artificial intelligence, Healthcare medical decision support systems, Non-communicable diseases, Sickle cell disease, Diabetes mellitus, COVID-19 pandemic