Название: Explainable Artificial Intelligence in Medical Decision Support Systems
Автор: A. Lucky Imoize, Jude Hemanth, Dinh-Thuan Do, Samarendra Nath Sur
Издательство: The Institution of Engineering and Technology
Год: 2022
Страниц: 545
Язык: английский
Формат: pdf (true)
Размер: 27.3 MB

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 (AI) 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.

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 (DL). 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 non-communicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. The Chapter 1 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.

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.

Written by an international team of experts, this book reviews state-of-the-art research and applications in its field for an audience of computer engineers, AI engineers, ICT professionals, and researchers in the field of Computer Science, Machine Learning, AI, and XAI, healthcare analytics, and related disciplines.