Clinician-Facing AI in the Wild: Taking Stock of the Sociotechnical Challenges and Opportunities for HCI
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Clinician-Facing AI in the Wild : Taking Stock of the Sociotechnical Challenges and Opportunities for HCI. / Zajac, Hubert D.; Li, Dana; Dai, Xiang; Carlsen, Jonathan F.; Kensing, Finn; Andersen, Tariq O.
I: ACM Transactions on Computer-Human Interaction, Bind 30, Nr. 2, 33, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 362454048