Opportunities and challenges of explainable artificial intelligence in medicine: toward causability for physicians, developers, and patients

An Zi Yen, Cheng Kuang Wu, Hsin Hsi Chen

研究成果: Chapter同行評審

摘要

Artificial intelligence (AI) has been successful in a wide variety of domains. However, the opacity of AI makes it hard for users to understand why a certain decision has been reached by the model, which can decrease user trust. Explainable AI, which addresses the implementation of transparency and interpretation of black box models, is especially crucial in the medical domain. In addition to mapping explainability to causability to deliver understandable explanations to users, we argue that the purpose and the presentation style of the explanation should depend on the stakeholder. In this chapter, we discuss the explanatory requirements of physicians, patients, and developers in the clinical flow. We also propose an explainable medical knowledge base diagnosis system to facilitate further development. To protect patient privacy, we present an innovative framework of explainable federated learning. Finally, we briefly mention the limitations and future directions of medical AI.

原文English
主出版物標題Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases
主出版物子標題Concept, Technology, Application and Perspectives
發行者Elsevier
頁面281-307
頁數27
ISBN(電子)9780323991360
ISBN(列印)9780323993760
DOIs
出版狀態Published - 1 1月 2023

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