Clinically Meaningful Explainability for NeuroAI: An ethical, technical, and clinical perspective
While explainable AI (XAI) is often heralded as a means to enhance transparency and trustworthiness in closed-loop neurotechnology for psychiatric and neurological conditions, its real-world prevalence remains low. Moreover, empirical evidence suggests that the type of explanations provided by current XAI methods often fails to align with clinicians' end-user needs. In this viewpoint, we argue that clinically meaningful explainability (CME) is essential for AI-enabled closed-loop medical neurotechnology and must be addressed from an ethical, technical, and clinical perspective. Instead of exhaustive technical detail, clinicians prioritize clinically relevant, actionable explanations, such...
| Authors | Laura Schopp, Ambra DImperio, Jalal Etesami et al. |
|---|---|
| Primary category | cs.CY |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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