Healthcare is a sector that can greatly benefit from Artificial Intelligence (AI) due to the critical nature of medical decisions and the demand for accuracy and efficiency. Advances in AI algorithms have shown promise in diagnosing conditions at early stages. To improve the transparency of such systems, Explainable AI (XAI) techniques have been explored. However, if not properly integrated, AI and XAI can introduce new challenges, such as reduced cognitive engagement or over-reliance on AI system outputs. While prior research has mainly focused on isolated tasks, such as AI-assisted diagnosis, this study takes a holistic approach by examining the impact of AI recommendations and explanations across multiple sequential decision points within clinical case scenarios. Thirty-four medical students completed two simulated clinical cases with varying levels of AI assistance. The scenarios reflected realistic workflows, requiring participants to prescribe diagnostic exams and make final diagnoses. Results indicate that AI recommendations and explanations can enhance medical performance, but do not consistently improve efficiency. In simpler cases, AI assistance supported appropriate exam prescription and reduced decision-making time. In more complex cases, explanations significantly improved diagnostic accuracy but also led to higher resource use, with participants ordering more unnecessary exams.
Abreu et al. (Mon,) studied this question.
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