Artificial intelligence (AI) is increasingly used in healthcare 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. Yet, 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: technical robustness, adaptation to end users, and alignment of explanations 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 healthcare 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 healthcare.
Savage et al. (2026) studied this question.