The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
Trust in artificial intelligence for medical diagnoses
Abstract
We present two online experiments investigating trust in artificial intelligence (AI) as a primary and secondary medical diagnosis tool and one experiment testing two methods to increase trust in AI. Participants in Experiment 1 read hypothetical scenarios of low and high-risk diseases, followed by two sequential diagnoses, and estimated their trust in the medical findings. In three between-participants groups, the first and second diagnoses were given by: human and AI, AI and human, and human and human doctors, respectively. In Experiment 2 we examined if people expected higher standards of performance from AI than human doctors, in order to trust AI treatment recommendations. In Experiment 3 we investigated the possibility to increase trust in AI diagnoses by: (i) informing our participants that the AI outperforms the human doctor, and (ii) nudging them to prefer AI diagnoses in a choice between AI and human doctors. Results indicate overall lower trust in AI, as well as for diagnoses of high-risk diseases. Participants trusted AI doctors less than humans for first diagnoses, and they were also less likely to trust a second opinion from an AI doctor for high risk diseases. Surprisingly, results highlight that people have comparable standards of performance for AI and human doctors and that trust in AI does not increase when people are told the AI outperforms the human doctor. Importantly, we find that the gap in trust between AI and human diagnoses is eliminated when people are nudged to select AI in a free-choice paradigm between human and AI diagnoses, with trust for AI diagnoses significantly increased when participants could choose their doctor. These findings isolate control over one's medical practitioner as a valid candidate for future trust-related medical diagnosis and highlight a solid potential path to smooth acceptance of AI diagnoses amongst patients.
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“The results of previous empirical studies on patients’ acceptance or trust in AI are somewhat conflicting. Some studies have shown that participants are more accepting of AI if it is proven to be more accurate than providers26,29 or if the use of AI fits societal and cultural norms.35,36 However, other studies have found that trust in AI did not increase when participants were informed that the AI”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [34]
In the citing paper
“This result is only partly consistent with those of previous studies, which have shown that participants are more accepting of AI if it is applied in a lower risk setting.32,34,51”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [34]
Citations
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Not So Different After All: A Cross-Discipline View Of Trust
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“Not surprisingly, trust was found to underlie willingness to try automation, making it crucial for the successful implementation of AI for healthcare (Lee and Moray, 1994; Lee and See, 2004).”
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Clinically applicable deep learning for diagnosis and referral in retinal disease
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Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00132-1 · Cited by 400
Vo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Nga, Q., & Woode, M. E. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2023.116357 · Cited by 180
Explainable AI-based Alzheimer’s prediction and management using multimodal data
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Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics
Faghani, S., Khosravi, B., Zhang, K., Moassefi, M., Jagtap, J. M. M., Nugen, F., Vahdati, S., Kuanar, S. P., Rassoulinejad-Mousavi, S. M., Singh, Y., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.220061 · Cited by 104
Metadata
- Authors
- Juravle, G., Boudouraki, A., Terziyska, M., & Rezlescu, C.
- Affiliations
- Alexandru Ioan Cuza University (RO)
- University of Nottingham (GB)
- University College London (GB)
- Corresponding author
- Georgiana Juravle
- Venue
- Progress in Brain Research
- Publisher
- Elsevier
- Published
- 2020
- Volume
- 253
- Pages
- 263-282
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W3039044431
- Cited by
- 116
- References
- 54
- Citations by year
- 2026: 22 · 2025: 25 · 2024: 33 · 2023: 21 · 2022: 11 · 2021: 4
- FWCI
- 5.46
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Progress in brain research (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- trust in AI, diagnosis, second opinion, high-risk disease, nudging, patient acceptance of AI, human-AI collaboration
- MeSH terms
- Clinical Decision-Making, Adolescent, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Diagnosis, Female, Humans, Male, Middle Aged, Physician-Patient Relations, Diagnostic Techniques and Procedures, Trust, Young Adult
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