Expert Perspectives on Transparency and Trust in AI-Based Clinical Decision Support Systems for Chest X-Ray Diagnosis: Qualitative Study

Abstract Background The integration of AI-based Clinical Decision Support Systems (AI-CDSS) into clinical practice has expanded rapidly, particularly in medical imaging. Beyond algorithmic performance, growing evidence suggests that clinicians’ engagement with AI-CDSS is shaped by how transparently systems communicate their outputs, and how trust is formed through human-AI interaction. However, a limited understanding of how these concepts are perceived by end users and developers continues to hinder real-world implementation. Objective This study aims to explore expert perspectives on transparency and trust in AI-CDSS for chest X-ray diagnosis, with a focus on the two-dimensional lens: user interface and users’ mental models. Methods A qualitative study was conducted using semistructured interviews with a total of 21 experts, including 11 radiologists and 10 AI experts experienced in AI-CDSS. Data were analyzed thematically to explore factors perceived to influence transparent communication and trust. Results Three themes emerged: (1) perspective of transparency and trust, (2) user interface, and (3) users’ mental models. Findings reveal that trust is a dynamic calibration process developed through repeated use, feedback loops, and alignment with clinicians’ mental models, rather than algorithmic accuracy alone. Significant discrepancies were identified between how developers design system transparency and how clinicians interpret AI outputs. Conclusions This study demonstrates that transparency and trust are achieved through interpretable outputs, clear communication of system limitations, and alignment with clinical workflows. Designing trustworthy AI-CDSS requires integrating transparent interface design with consideration of users’ knowledge, experience, and attitudes to support effective human-AI interaction.