An interdisciplinary perspective on AI-supported decision making in medicine

Bibliographic Data

ID6456257
AuthorsJonas Ammeling (0000-0002-0335-1194), Marc Aubreville (0000-0002-5294-5247), Alexis Fritz (0009-0008-2191-8722), Angelika Kießig (0009-0004-1238-327X), Sebastian Krügel (0000-0003-1196-8220), Matthias Uhl (0000-0002-8838-4824)
Year2024
Volume81
Pages102791-102791
Publication date2024-12-05
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTechnology in Society (JOURNAL)
Journal identifiersISSN: 0160-791X • E-ISSN: 1879-3274
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.techsoc.2024.102791
OpenAlexW4405035317
LanguageEN
Citations received2
References cited87

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

Clinical decision making · Engineering ethics · Family medicine · Knowledge management · Management science · Medical decision making · Perspective (graphical · Artificial Intelligence in Healthcare and Education · Computer Science · Engineering · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Medicine · Artificial Intelligence

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