Efficiency Pitfalls of Explainable AI in Clinical Diagnostic and Treatment Human-AI Workflows

= 11) employed qualitative methods, including think-aloud protocols and interviews, to explore clinicians' experiences with AI in daily (treatment) workflows.ResultsIn Study 1, explanations did not improve accuracy but increased decision time, reducing efficiency. Trends suggested lower perceived usefulness and trust in the explanation condition. Qualitative data from Study 2 supported these findings; clinicians found explanations time-consuming and disruptive, questioning their practical value, especially for routine cases.ConclusionA critical trade-off exists between pursuing AI transparency and the operational demand for efficiency. Explanations, while well-intentioned, can function as efficiency pitfalls in time-pressured clinical practice, highlighting the boundary conditions and challenges in designing effective human-AI systems.ApplicationThese insights inform future AI system design, favoring adaptable, on-demand explanations tailored to user needs. Such a user-centric approach supports complex cases without impeding routine task efficiency.