Literature Review: A Study of XAI User Experience in Healthcare: Transparency and Doctor-Patient Trust Construction Based on AI-assisted Diagnosis

Abstract

This article thoroughly looks at the user experience of Explainable Artificial Intelligence (XAI) in the medical field. It focuses on how XAI works in making things clear, building trust between doctors and patients, and helping with AI-based diagnosis.The research shows that the user experience of AI in healthcare is complicated. It includes many aspects like how easy it is to use, trust, satisfaction, and moral issues. Also, different user groups have different needs.Being clear and able to be explained are the bases for building trust in AI-assisted diagnosis. This greatly increases users' acceptance of AI suggestions.When designing XAI systems, we must fully think about the trust relationship between doctors and patients. We need to make sure this relationship is strengthened, not weakened.In the way of doing research, user studies, conceptual frameworks, meta-analyses, and using mixed methods give different views for research in this area. Different kinds of ways to explain things have their own good and bad points. We should choose them according to specific situations and user groups. Moreover, user characteristics and personalization are increasingly important in XAI design, and relevant design principles are also evolving, emphasizing key elements such as actionability, personalization, and transparency. Future research should focus on the long - term impact of XAI on doctor - patient trust and patient outcomes, develop explanation methods suitable for different healthcare scenarios and user groups, deeply explore its ethical implications, conduct longitudinal studies, and promote the transformation of design principles into practical tools, so as to maximize the value of XAI in healthcare, improve medical diagnosis, enhance patient care, and strengthen the doctor - patient relationship.