Explainable Artificial Intelligence (AI) for Medical Imaging: A Framework for Bridging the AI Trust Gap.

Abstract

Artificial intelligence (AI) is increasingly used in health care but often lacks clinician and patient trust. Explainable AI (XAI) aims to clarify predictions and to make AI decisions more transparent, interpretable, and clinically actionable. However, current methods fall short. In this Perspective, we argue that for XAI to be clinically useful in medical imaging and to build trust with clinicians, it must satisfy three guiding principles: it must be technically robust, be adapted to the end users, and be aligned with the specific clinical task. We introduce a conceptual framework incorporating these principles to guide future XAI design and deployment based on expectations and shared responsibilities for developers, vendors, and health care institutions. By ensuring robustness, personalizing outputs, and aligning explanations with use cases, XAI can move beyond one-size-fits-all approaches to task- and user-centered design to support effective and trustworthy AI adoption in health care.