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

Leiser, Florian ORCID iD icon 1; Warsinsky, Simon Lukas ORCID iD icon 1; Schmidt-Kraepelin, Manuel 1; Thiebes, Scott ORCID iD icon 1; Sunyaev, Ali 1
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)


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 easily formalized and integrated, 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 m​​odels.​

Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 30.05.2023
Sprache Englisch
Identifikator KITopen-ID: 1000159230
Erschienen in Proceedings NeuroIS Retreat 2023 Vienna, Austria | May 30 - June 1. Ed.: F. Davis
Veranstaltung NeuroIS Retreat (2023), Wien, Österreich, 30.05.2023 – 01.06.2023
Verlag Springer
Seiten 245-253
Schlagwörter Intuition, Expert Decision-Making, EEG, Eye-Tracking, Informed Machine Learning, Medicine
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