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How Experts Rely on Intuition in Medical Image Annotation—A Study Proposal

In: Information Systems and Neuroscience

Author

Listed:
  • Florian Leiser

    (Karlsruhe Institute of Technology)

  • Simon Warsinsky

    (Karlsruhe Institute of Technology)

  • Manuel Schmidt-Kraepelin

    (Karlsruhe Institute of Technology)

  • Scott Thiebes

    (Karlsruhe Institute of Technology)

  • Ali Sunyaev

    (Karlsruhe Institute of Technology)

Abstract

Contemporary machine learning (ML) research discusses the benefits of including domain knowledge in data-driven models under the term informed ML. While scientific domain knowledge can be formalized and integrated easily, expert knowledge is rather tacit and informal. Intuition is considered a key driver of expert judgment but is especially difficult to measure and formalize. In this study, we propose a cognitive task analysis-inspired approach to investigate the role of intuition during medical image annotation with the aid of neurophysiological measurements. We aim to observe 15 experts during their annotation and analyze EEG and eye-tracking data to identify cues indicating intuition. This study should provide insights into expert decision-making and the role of intuition therein and serve as a first step toward a later formalization of expert judgment for expert-informed ML models.

Suggested Citation

  • Florian Leiser & Simon Warsinsky & Manuel Schmidt-Kraepelin & Scott Thiebes & Ali Sunyaev, 2024. "How Experts Rely on Intuition in Medical Image Annotation—A Study Proposal," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 253-261, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-58396-4_22
    DOI: 10.1007/978-3-031-58396-4_22
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