Augmenting Medical Diagnosis Decisions: Article accepted at ISR

The article „Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision Making Process with Artificial Intelligence“ has been accepted for publication in the journal Information Systems Research (UT Dallas 24 List, VHB-JOURQUAL A+).

Ekaterina Jussupow, Kai Spohrer, Armin Heinzl, and Joshua Gawlitza conducted a study on medical decisions with Artificial Intelligence (AI) that has now been accepted for publication in Information Systems Research. The study investigates the impact of AI systems on physicians' diagnostic decisions. It elaborates the decision-making processes of novice and experienced physicians when they make diagnostic decisions together with an AI. The authors conducted experiments with 68 novice and 12 experienced physicians who were confronted with an AI system that provided both correct and incorrect advice. Novice physicians as well as experienced physicians made more inaccurate diagnostic decisions when provided with incorrect AI advice than they made without advice at all. Based on think-aloud protocols, interviews, and questionnaires the authors show that different metacognitions play a key role in whether physicians interact successfully with AI advice or not. The study elicits several decision-making patterns and presents a process model of medical decision augmentation with AI advice. The findings suggest that wrong diagnostic decisions often result from shortcomings in metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI system (system-monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in an unsuitably superficial evaluation of AI advice. The study demonstrates how human decision makers can compensate for the errors of an AI and why they often fail to do so.