Medical decision support systems (MDSS) are computer-based programs that
analyse data within a patient's healthcare records to provide questions,
prompts, or reminders to assist clinicians at the point of care.
Inputting a patient's data, symptoms, or current treatment regimens into
an MDSS, clinicians are assisted with the identification or elimination
of the most likely potential medical causes, which can enable faster
discovery of a set of appropriate diagnoses or treatment plans.
Explainable AI (XAI) is a "white box" model of artificial intelligence
in which the results of the solution can be understood by the users, who
can see an estimate of the weighted importance of each feature on the
model's predictions, and understand how the different features interact
to arrive at a specific decision.
This book discusses XAI-based analytics for patient-specific MDSS as
well as related security and privacy issues associated with processing
patient data. It provides insights into real-world scenarios of the
deployment, application, management, and associated benefits of XAI in
MDSS. The book outlines the frameworks for MDSS and explores the
applicability, prospects, and legal implications of XAI for MDSS.
Applications of XAI in MDSS such as XAI for robot-assisted surgeries,
medical image segmentation, cancer diagnostics, and diabetes mellitus
and heart disease prediction are explored.
