Health artificial intelligence (AI) is often developed in high-stakes, data-scarce contexts, where both clinical validity and patient comprehension are critical; however, rigorous, multi-level evaluation of explanations in real-world patient-facing settings remains challenging. To enhance patient understanding and trust, we propose a practical blueprint for designing and evaluating medically aligned, patient-centered explanation (MAP-X). We propose this blueprint through MAP-X, a system that employs a large language model (LLM) with retrieval-augmented generation (RAG) to translate clinical assessments into an understandable interface. We conducted a three-phase evaluation following a multi-level validation framework: a functional evaluation of faithfulness, a clinician evaluation of workflow suitability, and a patient evaluation of perceived understanding and trust. Our findings suggest that MAP-X may support clinical adoption. In the patient study, MAP-X showed higher reported trust and a positive trend in explanation satisfaction. Interviews suggested clearer understanding of assessment results. Overall, MAP-X produced clinically relevant explanations with reasonable faithfulness and usability. Clinician oversight remains necessary.

Explainable AI (XAI) is increasingly being used in the healthcare domain. In health management, clinicians and patients are critical stakeholders, requiring tailored XAI explanations based on their unique needs. Our study investigates the differences in explanation needs between clinicians and patients and designs corresponding explanation interfaces for each group. Using a scenario-based approach, we assessed stakeholder-tailored needs, analyzed differences, and designed interfaces using theoretical frameworks. The results demonstrate diverse stakeholder motivations for seeking explanations, leading to varied requirements. The designed interfaces effectively address these requirements, as validated by the preference selection and qualitative feedback from clinicians and patients. Their suggestions provide design insights and highlight the divergent needs of these stakeholder groups. This study contributes practical and theoretical implications to XAI research, emphasizing the importance of understanding diverse stakeholder needs and incorporating relevant theoretical concepts into user-centered interface design.