Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support
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In a randomized vignette study, clinicians exposed to a systematically biased model had lower diagnostic accuracy, and explainability features did not fully mitigate this effect.
“In a randomized vignette study, clinicians exposed to a systematically biased model had lower diagnostic accuracy, and explainability features did not fully mitigate this effect [81,82].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [82]
Automation bias refers to the tendency of individuals to rely excessively on automated systems and to disregard or underutilize their own judgment or critical thinking skills.
“Automation bias refers to the tendency of individuals to rely excessively on automated systems, such as AI algorithms, and to disregard or underutilize their own judgment or critical thinking skills.42”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [42]
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Brown, K. E., Wrenn, J. O., Jackson, N. J., Cauley, M. R., Collins, B. X., Novak, L. L., Malin, B., & Ancker, J. S. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf235 · Cited by 2 · see citing works
Abdurrachim, D., Wee, A., Soon, G. S. T., Idowu, M. O., Kendall, T. J., Lek, S., Ho, G., Engel, S. S., Talukdar, S., Chin, C., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.101752 · Cited by 2 · see citing works
Integrating Artificial Intelligence in Systemic Therapy: Strategies for Effective Clinical Practice
Hertlein, K. M., & Rajaei, A. (2026) · International Journal of Systemic Therapy · doi:10.1080/2692398x.2026.2633648 · Cited by 2 · see citing works
Wei, Y., Li, Y., Hu, W., Kang, J., Han, Z. Y., Zhen, M., & Wang, C. (2026) · Frontiers in Oral Health · doi:10.3389/froh.2026.1741033 · Cited by 2 · see citing works
Extending the Fundamental Theorem of Biomedical Informatics for the AI era
Payne, P., Chen, J. H., & Longhurst, C. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag065 · Cited by 2 · see citing works
Self-regulating the use of large language models in clinical practice: a risk-stratified approach
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Ethical Considerations in Personal Health Large Language Models
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Trustworthy Multimodal Depression Screening via Cross-Attention Fusion and Calibrated Uncertainty
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Multidisciplinary research priorities for artificial intelligence in mental health: a call to action
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LLM Reasoning Does Not Protect Against Clinical Cognitive Biases - An Evaluation Using BiasMedQAPreprint
Kim, S. H., Ziegelmayer, S., Busch, F., Mertens, C. J., Keicher, M., Adams, L. C., Bressem, K. K., Braren, R., Makowski, M. R., Kirschke, J. S., et al. (2025) · medRxiv · doi:10.1101/2025.06.22.25330078 · Cited by 2 · see citing works
- Authors
- Khera, R., Simon, M. A., & Ross, J. S.
- Affiliations
- Yale New Haven Hospital (US)
- Northwestern Medicine (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2023-12-19
- Volume
- 330
- Issue
- 23
- Pages
- 2255
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W4389948084
- Cited by
- 222
- References
- 13
- Citations by year
- 2026: 128 · 2025: 67 · 2024: 26 · 2023: 1
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K23HL153775)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- automation bias, clinical decision support, clinical judgment
- MeSH terms
- Artificial Intelligence, Automation, Bias, Decision Support Systems, Clinical
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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