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Radiologists’ Usage of Diagnostic AI Systems

Author

Listed:
  • Ekaterina Jussupow

    (Universität Mannheim)

  • Kai Spohrer

    (Frankfurt School of Finance and Management)

  • Armin Heinzl

    (Universität Mannheim)

Abstract

While diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research.

Suggested Citation

  • Ekaterina Jussupow & Kai Spohrer & Armin Heinzl, 2022. "Radiologists’ Usage of Diagnostic AI Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 293-309, June.
    • Handle: RePEc:spr:binfse:v:64:y:2022:i:3:d:10.1007_s12599-022-00750-2
    DOI: 10.1007/s12599-022-00750-2
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    References listed on IDEAS

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    Cited by:

    1. Williams, Robin & Anderson, Stuart & Cresswell, Kathrin & Kannelønning, Mari Serine & Mozaffar, Hajar & Yang, Xiao, 2024. "Domesticating AI in medical diagnosis," Technology in Society, Elsevier, vol. 76(C).

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