TY - JOUR
T1 - To warrant clinical adoption AI models require a multi-faceted implementation evaluation
AU - van de Sande, Davy
AU - Chung, Eline Fung Fen
AU - Oosterhoff, Jacobien
AU - van Bommel, Jasper
AU - Gommers, Diederik
AU - van Genderen, Michel E.
PY - 2024
Y1 - 2024
N2 - Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to translate these advancements into clinical value and adoption at the bedside remains comparatively limited. This paper reviews the current use of implementation outcomes in randomized controlled trials evaluating AI-based clinical decision support and found limited adoption. To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.
AB - Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to translate these advancements into clinical value and adoption at the bedside remains comparatively limited. This paper reviews the current use of implementation outcomes in randomized controlled trials evaluating AI-based clinical decision support and found limited adoption. To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.
UR - http://www.scopus.com/inward/record.url?scp=85186851186&partnerID=8YFLogxK
U2 - 10.1038/s41746-024-01064-1
DO - 10.1038/s41746-024-01064-1
M3 - Article
AN - SCOPUS:85186851186
SN - 2398-6352
VL - 7
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 58
ER -