Clinically Meaningful Explainability for NeuroAI: An ethical, technical, and clinical perspective

A team of researchers including Laura Schopp and Marcello Ienca has published a critical viewpoint paper on arXiv, arguing that current explainable AI (XAI) methods are failing clinicians in the high-stakes field of neurotechnology. While XAI is promoted to build trust in closed-loop systems for conditions like depression or epilepsy, the authors note its real-world adoption remains low. The core issue is a mismatch: clinicians don't need exhaustive technical transparency but rather actionable, clinically relevant explanations that directly inform patient treatment decisions.

To bridge this gap, the paper introduces the concept of Clinically Meaningful Explainability (CME), which prioritizes clarity and actionability over technical completeness. The authors propose a concrete solution called the NeuroXplain reference architecture. This framework provides actionable design recommendations for future neurostimulation devices, focusing on interface visualizations that intuitively map AI outputs—like feature importance and input-output relationships—into formats doctors can readily use. The 20-page paper aims to directly inform both neurotechnology developers and regulatory bodies, ensuring that AI explanations serve the right stakeholders to ultimately lead to better and more trustworthy patient care.