TY - JOUR

T1 - Clinician-Facing AI in the Wild

T2 - Taking Stock of the Sociotechnical Challenges and Opportunities for HCI

AU - Zajac, Hubert D.

AU - Li, Dana

AU - Dai, Xiang

AU - Carlsen, Jonathan F.

AU - Kensing, Finn

AU - Andersen, Tariq O.

N1 - Publisher Copyright: © 2023 Copyright held by the owner/author(s).

PY - 2023

Y1 - 2023

N2 - Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, which has been recognised in HCI and related fields. Recent publications have begun to address the sociotechnical challenges of designing, developing, and successfully deploying clinician-facing ML-based systems. We conducted a qualitative systematic review and provided answers to the question: "How can HCI researchers and practitioners contribute to the successful realisation of ML in medical practice?"We reviewed 25 eligible papers that investigated the real-world clinical implications of concrete clinician-facing ML-based systems. The main contributions of this systematic review are: (1) an overview of the technical aspects of ML innovation and their consequences for HCI researchers and practitioners; (2) a description of the different roles that ML-based systems can take in clinical settings; (3) a conceptualisation of the main activities of medical ML innovation processes; (4) identification of five sociotechnical interdependencies that emerge from medical ML innovation; and (5) implications for HCI researchers and practitioners on how to mitigate the sociotechnical challenges of medical ML innovation.

AB - Artificial Intelligence (AI) in medical applications holds great promise. However, the use of Machine Learning-based (ML) systems in clinical practice is still minimal. It is uniquely difficult to introduce clinician-facing ML-based systems in practice, which has been recognised in HCI and related fields. Recent publications have begun to address the sociotechnical challenges of designing, developing, and successfully deploying clinician-facing ML-based systems. We conducted a qualitative systematic review and provided answers to the question: "How can HCI researchers and practitioners contribute to the successful realisation of ML in medical practice?"We reviewed 25 eligible papers that investigated the real-world clinical implications of concrete clinician-facing ML-based systems. The main contributions of this systematic review are: (1) an overview of the technical aspects of ML innovation and their consequences for HCI researchers and practitioners; (2) a description of the different roles that ML-based systems can take in clinical settings; (3) a conceptualisation of the main activities of medical ML innovation processes; (4) identification of five sociotechnical interdependencies that emerge from medical ML innovation; and (5) implications for HCI researchers and practitioners on how to mitigate the sociotechnical challenges of medical ML innovation.

KW - artificial intelligence

KW - clinician-facing systems

KW - conceptual framework

KW - health

KW - implementation

KW - machine learning

KW - real-world

KW - Systematic review

UR - http://www.scopus.com/inward/record.url?scp=85158099176&partnerID=8YFLogxK

U2 - 10.1145/3582430

DO - 10.1145/3582430

M3 - Journal article

AN - SCOPUS:85158099176

VL - 30

JO - ACM Transactions on Computer-Human Interaction

JF - ACM Transactions on Computer-Human Interaction

SN - 1073-0516

IS - 2

M1 - 33

ER -