Explainability and causability in digital pathology.

Autor: Plass M; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria., Kargl M; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria., Kiehl TR; Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany., Regitnig P; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria., Geißler C; DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany., Evans T; DAI-Labor, Agent Oriented Technologies (AOT), Technische Universität Berlin, Berlin, Germany., Zerbe N; Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany., Carvalho R; Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany., Holzinger A; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.; Human-Centered AI Lab, University of Natural Resources and Life Sciences Vienna, Vienna, Austria., Müller H; Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.
Jazyk: angličtina
Zdroj: The journal of pathology. Clinical research [J Pathol Clin Res] 2023 Jul; Vol. 9 (4), pp. 251-260. Date of Electronic Publication: 2023 Apr 12.
DOI: 10.1002/cjp2.322
Abstrakt: The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.
(© 2023 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)
Databáze: MEDLINE
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