Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence
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- Ekaterina Jussupow
- Business School, Area Information Systems, Chair of General Management and Information Systems, University of Mannheim, 68161 Mannheim, Germany;
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- Kai Spohrer
- Business School, Area Information Systems, Chair of General Management and Information Systems, University of Mannheim, 68161 Mannheim, Germany;
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- Armin Heinzl
- Business School, Area Information Systems, Chair of General Management and Information Systems, University of Mannheim, 68161 Mannheim, Germany;
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- Joshua Gawlitza
- Institute of Diagnostic and Interventional Radiology, Thoracic Imaging, University Hospital Rechts der Isar, Technical University Munich, 81675 Munich, Germany
書誌事項
- 公開日
- 2021-09
- DOI
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- 10.1287/isre.2020.0980
- 公開者
- Institute for Operations Research and the Management Sciences (INFORMS)
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説明
<jats:p> Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors. </jats:p>
収録刊行物
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- Information Systems Research
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Information Systems Research 32 (3), 713-735, 2021-09
Institute for Operations Research and the Management Sciences (INFORMS)
