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Automation bias: a systematic review of frequency, effect mediators, and mitigators
Open accessAbstract
Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.
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“The result is a cognitive moral hazard: epistemic responsibility is abdicated not because delegation is appropriate, but because the trainee cannot distinguish appropriate delegation from uncritical acceptance 29 .”
From AI-induced never-skilling in medical education (2026) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Automation bias, well described in clinical decision support contexts, can lead individuals to privilege automated recommendations even when flawed, contributing to errors of commission or omission and reducing vigilance in information seeking [47].”
From Design and evaluation of a faculty development workshop series on integrating generative artificial intelligence in medical education: mixed methods pilot study (2026) · cites this paper as [47]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For humans, post-hoc interaction with such outputs is opaque and coarse-grained: experts must reverseengineer the model's reasoning to correct errors, while novices risk automation bias and over-reliance on machine-generated authority [32, 44, 87].”
From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“These fi ndings relate to work on automation bias, algorithm aversion, and 'appropriate reliance' [5, 32, 44, 87].”
From Criticmate: Stagewise Human–AI Co-Critique in Single-Screen UI Evaluation (2026) · cites this paper as [32]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“HR professionals might utilize a system for decision support differently, as prior experience with personnel selection tasks is related to higher confidence in one’s own decisions, resulting in a lower reliance on the system and less automation bias (Goddard et al., 2012; Langer et al., 2021).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
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“This way, the design of the system can support the attention of decision makers by varying reliability over time (Goddard et al., 2012; Zerilli et al., 2019).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
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“Automation bias has been found to be a serious problem in contexts of AI-based decision support systems (Mosier and Skitka, 1999; Goddard et al., 2012; Lyell et al., 2018; Davis et al., 2020)”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“The core finding is broadly consistent: when humans receive an algorithmic recommendation, they defer to it at rates exceeding what would be warranted by the algorithm's demonstrated accuracy, even when available evidence contradicts the recommendation (Skitka, Mosier, and Burdick 1999;Goddard, Roudsari, and Wyatt 2012).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Goddard, Roudsari, and Wyatt 2012
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“These factors have also been deemed influential in a systematic review by Goddard et al. (2012).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
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“The risk of automation bias occurs in this process: a human being relies too heavily on the algorithm, even in the face of erroneous predictions (Goddard et al. 2011).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Goddard et al. 2011
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“In the conclusion of their systematic review, Goddard et al. (2012) propose that expertise may act on primary drivers to influence reliance on DSS, with those primary drivers being trust and confidence, again emphasising that expertise likely plays an indirect role on reliance behaviour.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
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“Expertise, expected to be a predictor of both trust and reliance (Goddard et al., 2012), has been shown to reduce trust and reliance on AI, even when the user's judgement is incorrect (Küper et al., 2024).”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
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In the citing paper
“Goddard et al. (2012) propose that heightened confidence in one's own decisions generally diminishes reliance on external support, identifying confidence as a primary influence on reliance behaviour alongside trust.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Goddard et al., 2012
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“This risk is amplified by automation bias, the tendency to over-rely on algorithmic output, by leading clinicians to accept erroneous recommendations without adequate scrutiny.17-21”
From Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained Physicians (2025) · cites this paper as [18]
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In the citing paper
“The principle of non-discrimination in algorithmic decision-making is the last to consider for completing the algorithmic legality fundamental requirements. This translates into a duty to avoid so-called automation biases 75 , preventing outcomes that might be discriminatory toward the addressee of administrative action.”
From Transparency Warranties for Algorithmic Decisions in Administrative Procedures. Synopsis between EU Law and the Italian Legal System (2026) · cites this paper as Goddard K, Roudsari A, Wyatt JC (2012)
The cited source says what the claim attributes to it.
In the citing paper
“Experimental lab studies have diagnosed this tendency across a number of research fields (Goddard, Roudsari, and Wyatt 2012).”
From Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2023) · cites this paper as Goddard, Roudsari, and Wyatt 2012
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In the citing paper
“However, systems to aid decision-making might especially be considered for novice HR professionals as these systems tend to improve the decision-making quality of less experienced decision makers (Goddard et al., 2012; Langer et al., 2021).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
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In the citing paper
“Automation bias has been found to lead to adverse effects on decision outcomes in several other contexts before (e.g., Parasuraman and Manzey, 2010; Goddard et al., 2012).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Goddard et al., 2012
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In the citing paper
“Even when AI operates at the level of recommendation rather than direct action, the mere presence of an algorithmic suggestion can systematically shift human judgment toward the algorithm's conclusion, a phenomenon known as automation bias (Goddard, Roudsari, and Wyatt 2012;Alon-Barkat and Busuioc 2023).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Goddard, Roudsari, and Wyatt 2012
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In the citing paper
“Human involvement therefore does not necessarily guarantee effective control (Goddard et al. 2012).”
From When Recruiters Meet Algorithms: Advice Taking and Bias in AI‐Assisted Recruitment (2026) · cites this paper as Goddard et al. 2012
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“This tendency is consistent with automation bias, in which people overrely on automated recommendations and accept them without sufficient independent verification (Goddard et al., 2012).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Goddard et al., 2012
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In the citing paper
“All had previously passed our standard psychological screener [30].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [30]
Experimental — accuracy not yet measuredIn the citing paper
“Automation bias can occur in busy clinical settings where clinicians may feel pressure to make rapid decisions, leading them to rely on AI-generated results as a shortcut rather than engaging in thorough analysis.44”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [44]
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“The effect of being presented with a high AI score may lead to overreliance on the AI system without a radiologist maintaining their own vigilance or lead to reduced attention to other suspicious areas (automation bias) (28).”
BackgroundChugunova, M., & Sele, D. (2022) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2022.101897 · Cited by 118
Jones, C. H. D., Thornton, J. R., & Wyatt, J. C. (2023) · Medical Law Review · doi:10.1093/medlaw/fwad013 · Cited by 122
Self-citationFairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Fabris, A., Baranowska, N., Dennis, M. J., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3696457 · Cited by 117
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 123
The unintended consequences of health information technology revisited
Ash, J., Berg, M., & Coiera, E. (2016) · Yearbook of Medical Informatics · doi:10.15265/iy-2016-014 · Cited by 101
“Of specific interest to decision support system use, automation biases or automation-induced complacency are increasingly recognized as an important source of decision error [52, 53].”
Automated Contouring and Planning in Radiation Therapy: What Is ‘Clinically Acceptable’?
Baroudi, H., Brock, K. K., Cao, W., Chen, X., Chung, C. D., Court, L. E., Basha, M. D. E., Farhat, M., Gay, S., Gronberg, M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13040667 · Cited by 112
Medical Education in the Electronic Medical Record (EMR) Era
Tierney, M. J., Pageler, N. M., Kahana, M. D., Pantaleoni, J. L., & Longhurst, C. (2013) · Academic Medicine · doi:10.1097/acm.0b013e3182905ceb · Cited by 109
Arnold, M. H. (2021) · Journal of Bioethical Inquiry · doi:10.1007/s11673-020-10080-1 · Cited by 121
Automation bias in electronic prescribing
Lyell, D., Magrabi, F., Raban, M. Z., Pont, L. G., Baysari, M. T., Day, R. O., & Coiera, E. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0425-5 · Cited by 103
“There are multiple possible causes of AB, [8, 9] and the literature is currently unclear regarding which, or all, of these are genuinely causal, and under which circumstances.”
BackgroundNourani, M., Kabir, S., Mohseni, S., & Ragan, E. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5284 · Cited by 110
Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act
Kotter, E., D’Antonoli, T. A., Cuocolo, R., Hierath, M., Huisman, M., Klontzas, M. E., Martí‐Bonmatí, L., May, M. S., Neri, E., Nikolaou, K., et al. (2025) · Insights into Imaging · doi:10.1186/s13244-025-01905-x · Cited by 103
Artificial intelligence in medicine and the disclosure of risks
Kiener, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01085-w · Cited by 97
“First, psychological research has shown that people have an ‘auto-mation bias’, i.e. people display an unjustified reliance on machines over human decisions and are likely to think that AI is free from the frailties of human choice (Goddard et al. 2012).”
BackgroundBrady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 106
“70 – 73 These biases need to be both clari fi ed and managed to ensure the AI ’ s output does not negatively in fl uence the radiologist ’ s judgement.”
BackgroundLyell, D., Coiera, E., Chen, J., Shah, P., & Magrabi, F. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100301 · Cited by 95
Evidence, ethics and the promise of artificial intelligence in psychiatry
McCradden, M., Hui, K., & Buchman, D. Z. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108447 · Cited by 97
“51 Emerging evidence indicates an exacerbation of automation bias—a well-characterised problem over over-reliance on computational systems 52 —with AI systems.”
BackgroundThe loopholes of algorithmic public services: an “intelligent” accountability research agenda
Bracci, E. (2023) · Accounting, Auditing & Accountability Journal · doi:10.1108/aaaj-06-2022-5856 · Cited by 89
Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?
Springer, A., & Whittaker, S. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3374218 · Cited by 84
“All had previously passed our standard psychological screener [30].”
BackgroundSanchez‐Martinez, S., Cámara, Ó., Piella, G., Čikeš, M., González‐Ballester, M. Á., Miron, M., Vellido, A., Gómez, E., Fraser, A. G., & Bijnens, B. H. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.765693 · Cited by 112
“As shown by Goddard et al. (44), when the ML solution is reliable it augments human performance, but when the solution is incorrect human errors increase.”
BackgroundFairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86
Machine heuristic: concept explication and development of a measurement scale
Yang, H. J., & Sundar, S. S. (2024) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmae019 · Cited by 104
The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation
Rinta-Kahila, T., The University of Queensland Business School, Penttinen, E., Aalto University School of Business, Salovaara, A., Aalto University / University of Helsinki, Soliman, W., University of Agder, N. /. U. O. J., Ruissalo, J., & Aalto University School of Business (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00829 · Cited by 86
Antaki, F., Milad, D., Chia, M. A., Giguère, C., Touma, S., El‐Khoury, J., Keane, P. A., & Duval, R. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-324438 · Cited by 86
“21 In our study, the grading task was multidimensional and required the graders to weight multiple factors at once.”
Wyatt, J. C. (2018) · BMC Medicine · doi:10.1186/s12916-018-1211-7 · Cited by 61
Self-citationGreenhalgh, T., Morris, L., Wyatt, J. C., Thomas, G., & Gunning, K. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2013.01.002 · Cited by 79
Self-citationPovyakalo, A. A., Alberdi, E., Strigini, L., & Ayton, P. (2013) · Medical Decision Making · doi:10.1177/0272989x12465490 · Cited by 79
The right to refuse diagnostics and treatment planning by artificial intelligence
Ploug, T., & Holm, S. (2019) · Medicine Health Care and Philosophy · doi:10.1007/s11019-019-09912-8 · Cited by 85
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 92
“It has been demonstrated [255], [256] that in many practical scenarios, human operators — especially when under time pressure and multi-tasking — quickly place too much trust in an automated system which has performed reliably in a large number of cases, thereby increasing the likelihood of a…”
BackgroundAI literacy for ethical use of chatbot: Will students accept AI ethics?
Kajiwara, Y., & Kawabata, K. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100251 · Cited by 116
Parvinian, B., Scully, C. G., Wiyor, H. D., Kumar, A., & Weininger, S. (2017) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000002329 · Cited by 76
Sullivan, B. A., Beam, K., Vesoulis, Z. A., Aziz, K. B., Husain, A. N., Knake, L. A., Moreira, A. G., Hooven, T. A., Weiss, E. M., Carr, N., et al. (2023) · Journal of Perinatology · doi:10.1038/s41372-023-01848-5 · Cited by 86
“Additionally, we must avoid creating a generation of physicians with automation bias [101].”
New Information Technology and Implicit Bias
Elsbach, K. D., & Stigliani, I. (2019) · Academy of Management Perspectives · doi:10.5465/amp.2017.0079 · Cited by 74
“…in new information technology appearance 5 trustworthy ” and “ humanness in new information technology performance 5 trustworthy ” indicate that potential users of these technologies hold the general belief that new information technology is nonhuman and alien (Goddard, Roudsari, & Wyatt, 2012).”
BackgroundNot all biases are bad: equitable and inequitable biases in machine learning and radiology
Pot, M., Kieusseyan, N., & Prainsack, B. (2021) · Insights into Imaging · doi:10.1186/s13244-020-00955-7 · Cited by 78
Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Radiology Artificial Intelligence · doi:10.1148/ryai.230513 · Cited by 88
“As discussed in Section 6 (Human-AI Interaction), one crucial issue is the potential introduction of human–computer biases into the radiologic interpretation (59, 70–73).”
BackgroundHow Should AI Be Developed, Validated, and Implemented in Patient Care?
Anderson, M., & Anderson, S. (2019) · The AMA Journal of Ethic · doi:10.1001/amajethics.2019.125 · Cited by 73
The role of agent autonomy in using decision support systems at work
Ulfert, A., Antoni, C. H., & Ellwart, T. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106987 · Cited by 84
Honeycutt, D. R., Nourani, M., & Ragan, E. D. (2020) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v8i1.7464 · Cited by 86
Explanation matters: An experimental study on explainable AI
Hamm, P., Klesel, M., Coberger, P., & Wittmann, H. F. (2023) · Electronic Markets · doi:10.1007/s12525-023-00640-9 · Cited by 75
“This in turn can lead to undesirable effects such as “automation bias,” which describes a tendency to rely too much on automated processes (Goddard et al., 2012).”
Deep Automation Bias: How to Tackle a Wicked Problem of AI?
Strauß, S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5020018 · Cited by 83
The effects of explanations on automation bias
Vered, M., Livni, T., Howe, P. D. L., Miller, T., & Sonenberg, L. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103952 · Cited by 83
How Beginning Programmers and Code LLMs (Mis)read Each Other
Nguyen, S., Babe, H. M., Zi, Y., Guha, A., Anderson, C. J., & Feldman, M. Q. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642706 · Cited by 120
“Beginning students are particularly likely to err in this way: they may struggle to understand generated code, and their lack of confidence in their own abilities may make them trust the automated system over their own judgment (an example of automation bias [18, 30, 32, 91]).”
Temsah, M.-H., Alhuzaimi, A. N., Almansour, M., Aljamaan, F., Alhasan, K., Batarfi, M. A., Altamimi, I., Alharbi, A., Alsuhaibani, A. A., Alwakeel, L., et al. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02072-0 · Cited by 66
“The investigator used the prompt to draw “detailed and accurate anatomy illustration of the human heart” on the three The inaccuracy issues may stem from DALL·E 3 possibly being trained on unrepresentative data, leading to a risk of overfitting to inaccurate disease images from automation bias [45].”
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67
Cognitive Biases in Fact-Checking and Their Countermeasures: A Review
Soprano, M., Roitero, K., Barbera, D. L., Ceolin, D., Spina, D., Demartini, G., & Mizzaro, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103672 · Cited by 74
What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
Hildt, E. (2025) · Bioengineering · doi:10.3390/bioengineering12040375 · Cited by 74
“Automation bias when using AI-based CDSSs may tempt clinicians to disregard contrary indications and data and reverse a correct decision they have already made and thus introduce errors [88–90].”
Designing Human-Agent Collaborations: Commitment, responsiveness, and support
Cila, N. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517500 · Cited by 65
“Overestimation was hazardous in these two cases; but it can lead to disastrous, even lethal, outcomes in critical systems and it should be avoided at all costs [57].”
BackgroundWang, H. E., Landers, M., Adams, R., Subbaswamy, A., Kharrazi, H., Gaskin, D. J., & Saria, S. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac065 · Cited by 63
Horowitz, M. C., & Kahn, L. (2024) · International Studies Quarterly · doi:10.1093/isq/sqae020 · Cited by 68
Reliance, trust and heuristics in judgmental forecasting
Alvarado, J., & Barrero, L. H. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.047 · Cited by 66
Automation bias in public administration – an interdisciplinary perspective from law and psychology
Ruschemeier, H., & Hondrich, L. J. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101953 · Cited by 59
Check the box! How to deal with automation bias in AI-based personnel selection
Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1118723 · Cited by 59
“Automation bias has been found to be a serious problem in contexts of AI-based decision support systems (Mosier and Skitka, 1999; Goddard et al., 2012; Lyell et al., 2018; Davis et al., 2020), and violates ethical recommendations (Hunkenschroer and Luetge, 2022) as well as legal requirements like Article 22 of the GDP…”
BackgroundInfluential
Metadata
- Authors
- Goddard, K., Roudsari, A., & Wyatt, J. C.
- Affiliations
- City, University of London (GB)
- University of Warwick (GB)
- Corresponding author
- Goddard, Kate
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01
- Volume
- 19
- Issue
- 1
- Pages
- 121-127
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC3240751
- OpenAlex
- W2281090488
- Cited by
- 1,162
- References
- 77
- Citations by year
- 2026: 513 · 2025: 180 · 2024: 111 · 2023: 88 · 2022: 78 · 2021: 61 · 2020: 39 · 2019: 29 · 2018: 23 · 2017: 8 · 2016: 7 · 2015: 4 · 2014: 6 · 2013: 11 · 2012: 4
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- automation bias, clinical decision support systems, human-automation interaction, over-reliance on automation, trust in automation, cognitive style, workload, task complexity, time constraints, user accountability, DSS design, clinical decision-making
- MeSH terms
- Attitude to Computers, Automation, Aviation, Humans, Task Performance and Analysis, 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.
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