TY - JOUR
T1 - Human–machine teaming is key to AI adoption
T2 - clinicians’ experiences with a deployed machine learning system
AU - Henry, Katharine E.
AU - Kornfield, Rachel
AU - Sridharan, Anirudh
AU - Linton, Robert C.
AU - Groh, Catherine
AU - Wang, Tony
AU - Wu, Albert
AU - Mutlu, Bilge
AU - Saria, Suchi
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians’ autonomy and support them across their entire workflow.
AB - While a growing number of machine learning (ML) systems have been deployed in clinical settings with the promise of improving patient care, many have struggled to gain adoption and realize this promise. Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians’ autonomy and support them across their entire workflow.
UR - http://www.scopus.com/inward/record.url?scp=85134495392&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134495392&partnerID=8YFLogxK
U2 - 10.1038/s41746-022-00597-7
DO - 10.1038/s41746-022-00597-7
M3 - Article
C2 - 35864312
AN - SCOPUS:85134495392
SN - 2398-6352
VL - 5
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 97
ER -