The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support
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In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
The false hope of current approaches to explainable artificial intelligence in health care
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162
Explainability and artificial intelligence in medicine
Reddy, S. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00029-2 · Cited by 246
Machine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou, E. K., & Khera, R. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01985-3 · Cited by 190
Self-citationDetection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.062646 · Cited by 150
Self-citationSevere aortic stenosis detection by deep learning applied to echocardiography
Holste, G., Oikonomou, E. K., Mortazavi, B. J., Coppi, A. C., Faridi, K. F., Miller, E. J., Forrest, J. K., McNamara, R. L., Lucila Ohno-machado, A., Yuan, N., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad456 · Cited by 149
Self-citationApproaching autonomy in medical artificial intelligence
Bitterman, D. S., Aerts, H. J. W. L., & Mak, R. H. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30187-4 · Cited by 107
Characterizing the clinical adoption of medical AI devices through U.S. insurance claims
Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103
AI in Medicine—JAMA’s Focus on Clinical Outcomes, Patient-Centered Care, Quality, and Equity
Khera, R., Butte, A. J., Berkwits, M., Hswen, Y., Flanagin, A., Park, H., Curfman, G., & Bibbins-Domingo, K. (2023) · JAMA · doi:10.1001/jama.2023.15481 · Cited by 78
Self-citationPrevention of Bias and Discrimination in Clinical Practice Algorithms
Shachar, C., & Gerke, S. (2023) · JAMA · doi:10.1001/jama.2022.23867 · Cited by 53
Research works printed but not linked: 1
- Jabbour, Measuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study., JAMA
Cited by
Sorted by citations
AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence
Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA · doi:10.1001/jama.2025.18490 · Cited by 195
Self-citationKoçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168
“42 In the context of AI in medical imaging, automation bias can manifest when clinicians or radiologists place un-due trust in the outputs or recommendations provided by AI algorithms, leading them to overlook potentially important information or make errors in diagnosis or treatment planning.”
The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges
Bulut, O., & Beiting-Parrish, M. (2024) · Chinese/English Journal of Educational Measurement and Evaluation · doi:10.59863/miql7785 · Cited by 272
Oculomics: Current concepts and evidence
Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101350 · Cited by 97
Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance
Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 100
Self-citation
Metadata
- 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 › Human Factors and Ergonomics › 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
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