Designing AI-Based Work Processes: How the Timing of AI Advice Affects Diagnostic Decision Making
通过实验研究AI建议时机对医生诊断的影响，发现事后建议（先诊断后给AI意见）比事前建议（与临床信息同时给出）更能提升诊断准确性和校准度，原因在于更充分的信息处理和认知参与。
Although clinical artificial intelligence (AI) systems can augment medical diagnosis decisions by providing competent second opinions, how to effectively integrate AI into routine diagnostic processes, such as when to present AI advice to human physicians, remains largely unexplored. Therefore, our research experimentally examines how the timing of AI advice affects diagnostic decision making using a think-aloud approach. Physicians perform medical diagnoses under three conditions: ex post advice (AI advice given after an initial diagnosis), ex ante advice (AI advice given concurrently with clinical information), and a control condition (no AI advice). Our results indicate that the timing of AI advice significantly affects diagnostic accuracy and calibration, with the ex post advice condition yielding the best performance and the control condition the worst. We then conduct several analyses to disentangle the underlying mechanism. We reveal that the superior diagnostic quality in the ex post advice condition can be attributed to more thorough clinical information processing and more active cognitive engagement with AI’s reasoning rationale. As a result, participants in the ex post advice condition are more capable of differentiating correct from incorrect AI advice than those in the ex ante advice condition. Additionally, they benefit more from high-quality AI advice that contradicts their initial diagnoses. To gain additional insights, we estimate the heterogeneous treatment effects based on physician and clinical case characteristics. Our findings underscore the importance of presenting AI advice at appropriate times during routine diagnostic processes to achieve successful decision augmentation with AI advice. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the National University of Singapore [Grants Dean Strategic Fund - Health Informatics (HIIOT)/E and NSCP/ N-171-000-499-001] and the National Natural Science Foundation of China [Grant 72301279]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01454 .