TY - JOUR
T1 - Why we should not mistake accuracy of medical AI for efficiency
AU - Jongsma, Karin Rolanda
AU - Sand, Martin
AU - Milota, Megan
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/3/4
Y1 - 2024/3/4
N2 - In the medical literature, promising results regarding accuracy of medical AI are presented as claims for its potential to increase efficiency. This elision of concepts is misleading and incorrect. First, the promise that AI will reduce human workload rests on a too narrow assessment of what constitutes workload in the first place. Human operators need new skills and deal with new responsibilities, these systems need an elaborate infrastructure and support system that all contribute to an increased amount of human work and short-term efficiency wins may become sources of long-term inefficiency. Second, for the realization of increased efficiency, the human-side of technology implementation is determinate. Human knowledge, competencies and trust can foster or undermine efficiency. We conclude that is important to remain conscious and critical about how we talk about expected benefits of AI, especially when referring to systemic changes based on single studies.
AB - In the medical literature, promising results regarding accuracy of medical AI are presented as claims for its potential to increase efficiency. This elision of concepts is misleading and incorrect. First, the promise that AI will reduce human workload rests on a too narrow assessment of what constitutes workload in the first place. Human operators need new skills and deal with new responsibilities, these systems need an elaborate infrastructure and support system that all contribute to an increased amount of human work and short-term efficiency wins may become sources of long-term inefficiency. Second, for the realization of increased efficiency, the human-side of technology implementation is determinate. Human knowledge, competencies and trust can foster or undermine efficiency. We conclude that is important to remain conscious and critical about how we talk about expected benefits of AI, especially when referring to systemic changes based on single studies.
UR - http://www.scopus.com/inward/record.url?scp=85186873533&partnerID=8YFLogxK
U2 - 10.1038/s41746-024-01047-2
DO - 10.1038/s41746-024-01047-2
M3 - Comment/Letter to the editor
C2 - 38438477
AN - SCOPUS:85186873533
SN - 2398-6352
VL - 7
JO - NPJ DIGITAL MEDICINE
JF - NPJ DIGITAL MEDICINE
IS - 1
M1 - 57
ER -