Operationalizing selective transparency using progressive disclosure in artificial intelligence clinical diagnosis systems

Explainable AI (XAI) is critical for clinical decision support systems (AI-CDSS) in healthcare, but current approaches often neglect the usability of explanations from a human–computer interaction (HCI) perspective. We investigate progressive disclosure as a strategy for selective transparency to provide effective explanations without overwhelming users. This paper presents a user-centered design of AI-CDSS interface prototypes that incorporate interactive explanation features (e.g., keyword highlighting of medical terms and interactive causal diagrams) and empathy-oriented nudges (e.g., supportive prompts and icons). We evaluated these prototypes through interviews with medical professionals and students, followed by a user study with general users, to assess their impact on understanding, trust, and satisfaction. Our findings suggest that progressive, on-demand disclosure of explanation details may help users manage information load and better follow the AI’s reasoning process. While several interface features were well received, some elements such as affective cues like emojis elicited skepticism, particularly in clinical contexts, which underscores the importance of context-sensitive design choices.