An interdisciplinary perspective on AI-supported decision making in medicine

Jonas Ammeling, Marc Aubreville, Alexis Fritz, Angelika Kießig, Sebastian Krügel and Matthias Uhl

Technology in Society, 2025, vol. 81, issue C

Abstract: Artificial intelligence (AI)-supported medical diagnosis offers the potential to utilize the collaborative intelligence of context-sensitive humans and narrowly focused machines for patients’ benefit. The employment of machine-learning-based decision-support systems (MLDSS) in medicine, however, raises important multidisciplinary challenges that cannot be addressed in isolation. We discuss three disciplinary perspectives on the topic and their interplay. Ethical issues arise at the level of changing responsibility structures in healthcare. Behavioral issues relate to the actual impact that the system has on physicians. Technical issues arise with respect to the training of a machine learning (ML) model that gives accurate advice. We argue that the interaction between physicians and MLDSS including the concrete design of the interface in which this interaction occurs can only be considered at the intersection of all three disciplines.

Keywords: Machine learning; Decision-support system; Human-machine interaction; Medical image diagnosis; Ethically aligned design; Value-based design (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x24003397

DOI: 10.1016/j.techsoc.2024.102791

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