Why we do need explainable AI for healthcare

Abstract

The recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy, has also come under scrutiny as part of this discussion. Some experts in the medical AI space debate the reliability of Explainable AI techniques, expressing concerns on their use and inclusion in guidelines and standards. Revisiting such criticisms, this article offers a balanced perspective on the utility of Explainable AI, focusing on the specificity of clinical applications of AI and placing them in the context of healthcare interventions. Against its detractors and despite valid concerns, we argue that the Explainable AI research program is still central to human-machine interaction and ultimately a useful tool against loss of control, a danger that cannot be prevented by rigorous clinical validation alone.

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