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

Florian Leiser (), Simon Warsinsky (), Manuel Schmidt-Kraepelin (), Scott Thiebes () and Ali Sunyaev ()
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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

A chapter in Information Systems and Neuroscience, 2024, pp 253-261 from Springer

Abstract: 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.

Keywords: Intuition; Expert decision-making; EEG; Eye-tracking; Informed machine learning; Medicine (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-58396-4_22

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DOI: 10.1007/978-3-031-58396-4_22

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