Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals
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Authors
Bienefeld, Nadine
Boss, Jens Michael
Willms, Jan
Keller, Emanuela
Author (Corporation)
Publication date
22.05.2023
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01A - Journal article
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Parent work
npj Digital Medicine
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Volume
6
Issue / Number
1
Pages / Duration
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Abstract
Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and requirements they may have. This paper presents the findings of a longitudinal multi-method study involving 112 developers and clinicians co-designing an XAI solution for a clinical decision support system. Our study identifies three key differences between developer and clinician mental models of XAI, including opposing goals (model interpretability vs. clinical plausibility), different sources of truth (data vs. patient), and the role of exploring new vs. exploiting old knowledge. Based on our findings, we propose design solutions that can help address the XAI conundrum in healthcare, including the use of causal inference models, personalized explanations, and ambidexterity between exploration and exploitation mindsets. Our study highlights the importance of considering the perspectives of both developers and clinicians in the design of XAI systems and provides practical recommendations for improving the effectiveness and usability of XAI in healthcare.
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Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
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ISBN
ISSN
2398-6352
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
BIENEFELD, Nadine, Jens Michael BOSS, Rahel LÜTHY, Dominique BRODBECK, Jan AZZATI, Mirco BLASER, Jan WILLMS und Emanuela KELLER, 2023. Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals. npj Digital Medicine. 22 Mai 2023. Bd. 6, Nr. 1. DOI 10.1038/s41746-023-00837-4. Verfügbar unter: https://doi.org/10.26041/fhnw-5438