Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities

Explainability is essential for clinical AI, not optional: As AI models become more complex and show promise in matching or exceeding human performance in diagnosis and disease prognosis, their opacity creates significant concerns. The paper emphasizes that explainability—an AI system's ability to explain its decision-making logic—is crucial for ensuring trust and reliability in clinical risk prediction models.

Explainability encompasses more than just interpretability: Beyond simply explaining how a model makes decisions, responsible clinical AI must also address fairness, bias detection, transparency, and trustworthiness. The paper clarifies that these concepts are often conflated but represent distinct aspects of responsible AI that all matter for patient safety.

Multiple validation approaches strengthen confidence in AI explanations: The paper stresses the importance of both quantitative and clinical evaluation, external validation across different patient populations, and combining diverse interpretability methods rather than relying on a single explanation technique. Testing with synthetic datasets of known characteristics can further verify that explanations are reliable.

Transparency and reproducibility are foundational: The paper highlights that open access to code and resources is essential for the field to validate explainability methods and build trust in clinical AI systems, preventing the "black box" problem from persisting in healthcare applications.