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Explainable AI (XAI) in clinical risk prediction helps make applied machine learning models transparent and clinically accountable. This tutorial covers recent progress across EHR, text, imaging, genomics, and multimodal data, with a later focus on emerging large language model (LLM) systems. We aimed to standardise terminology and distinguish terms such as interpretability, explanation, fairness, bias, trust, and transparency. We also provide a decision map linking methods to evaluation requirements. We review 97 unique studies (2019–2024) using a scheme that distinguishes alignment with knowledge (AK), expert review (ER), decision impact (DI), workflow integration (WI), and patient-facing (PF) assessment. We find that SHAP with tree ensembles is the most popular method in tabular EHRs, attention and CAM methods are common in imaging, and saliency-plus enrichment testing in genomics. A minority of papers report clinician-involved validation, and reporting of rater agreement, calibration, and subgroup effects is inconsistent. We, thus, propose a tiered, modality-agnostic framework, comprising AK+ER as the minimum standard, followed by DI (accuracy, time, confidence), and then WI (usage, outcomes), and provide design checklists, metrics, and pitfalls. Our aim is a roadmap, grounded in clinical practice, for developing, stress-testing, and deploying XAI in risk prediction.
Mesinovic et al. (Tue,) studied this question.