An interdisciplinary perspective on AI-supported decision making in medicine
Jonas Ammeling, Marc Aubreville, Alexis Fritz, Angelika Kießig, Sebastian Krügel, Matthias Uhl
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. • We propose an interdisciplinary research program on AI-supported medicine. • Perspectives from ethics, behavioral and computer science are discussed. • Human-centered recommender systems require these perspectives to be integrated.