What we talk about when we talk about trust: Theory of trust for AI in healthcare
Open accessAbstract
Artificial intelligence (AI) is at the forefront of innovation in medicine. Researchers and AI developers have often claimed that "trust" is a critical determinant of the successful adoption of AI in medicine. Despite the pivotal role of trust and the emergence of an array of expert-informed guidelines on how to design and implement "trustworthy AI" in medicine, we found little common understanding across these guidelines on what constitutes user trust in AI and what the requirements are for its realization. In this article, we call for a conceptual framework of trust in health-related AI which is based not just on expert opinion, but first and foremost on sound empirical research and conceptual rigor. Only with a well-grounded and comprehensive understanding of the trust construct, we will be able to inform AI design and acceptance in medicine in a meaningful way.
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Hesitance to adopt AI technologies could directly conflict with the principle of common good orientation, as it hinders the widespread and equitable implementation of AI that could benefit the entire community.
“This hesitance could directly conflict with the principle of common good orientation, as it hinders the widespread and equitable implementation of AI that could benefit the entire community [25].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [25]
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- Authors
- Gille, F., Jobin, A., & Ienca, M.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Gille, Felix
- Venue
- Intelligence-Based Medicine
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 1-2
- Pages
- 100001
- ISSN
- 2666-5212 (ISSN-L)
- OpenAlex
- W3043673199
- Cited by
- 159
- References
- 24
- Citations by year
- 2026: 21 · 2025: 29 · 2024: 49 · 2023: 35 · 2022: 13 · 2021: 12
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Intelligence-Based Medicine (publishedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- trust in AI, trustworthy AI, AI adoption in medicine, user trust, AI acceptance, artificial intelligence in healthcare
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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