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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.
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“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]
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“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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“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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“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)
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“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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“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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“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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“Distrust can rise for opaque systems (Ferrario et al. 2021), while excessive trust may result from ‘automation bias’ (Goddard et al. 2012).”
UNDERSTANDING HUMAN OVER-RELIANCE ON TECHNOLOGY.
Grissinger, M. C. (2017) · PubMed Central · Cited by 42
Brauner, P., Philipsen, R., Valdez, A. S. C., & Ziefle, M. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1581258 · Cited by 41
“…the positive e ff ects of good interface design to compensate system errors are well studied in some domains, such as medical informatics or aviation (Goddard, Roudsari, and Wyatt 2012), the emerging fi elds of the Industrial Internet and CPPSs often neglect the human factors perspective,…”
BackgroundHoward, A., Aston, S., Gerada, A., Reza, N., Bincalar, J., Mwandumba, H. C., Butterworth, T., Hope, W. W., & Buchan, I. E. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00221-2 · Cited by 43
Cecil, J., Lermer, E., Hudecek, M. F. C., Sauer, J., & Gaube, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-60220-5 · Cited by 37
“Reliance on advice from decision support systems without searching for or ignoring contradictory information is called automation bias 21,22 .”
Evidence-based medicine and big genomic data
Ioannidis, J. P. A., & Khoury, M. J. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy065 · Cited by 41
“Simply accruing more data and using more automation may even solidify the various forms of automation bias (32) without offering a clinical advantage.”
BackgroundSevgi, M., Antaki, F., & Keane, P. A. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-325046 · Cited by 42
“58 Incorrect interpretations or incomplete retrieval of information from the guidelines could lead to misguided clinical decisions.”
BackgroundScott, I. A., Sullivan, C. M., & Staib, A. N. (2018) · Australian Health Review · doi:10.1071/ah17153 · Cited by 47
VPNET: Variable Projection Networks
Kovács, P., Bognár, G., Huber, C., & Huemer, M. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500544 · Cited by 38
“However, special care should be taken to avoid automation bias when these approaches are applied to real-world problems.(104) These clinical decision-support systems are difficult to validate, since this requires medical expertise and vast amounts of data.”
Goankar, B., Cook, K., & Macyszyn, L. J. (2020) · AI Ethics Journal · doi:10.47289/aiej20200916 · Cited by 43
Murtagh, N., Gatersleben, B., Cowen, L., & Uzzell, D. L. (2015) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2015.04.002 · Cited by 36
AI-induced never-skilling in medical education
Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04438-y · Cited by 71
“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 .”
A Framework for Interpretability in Machine Learning for Medical Imaging
Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 41
“When users’ levels of trust in algorithmic decision-making aids are mis-calibrated, the result manifests as either automation bias (too much trust) or algorithmic aversion (too much distrust) [28], [44], [92].”
BackgroundKazemitabaar, M., Huang, O., Suh, S., Henley, A. Z., & Grossman, T. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712104 · Cited by 70
“Extensive research, such as studies involving doctors using clinical decision support systems, has demonstrated the per-vasiveness of this bias [35].”
Opportunities and risks of artificial intelligence in patient portal messaging in primary care
Biro, J. M., Handley, J. L., Malcolm McCurry, J., Visconti, A., Weinfeld, J., Gregory Trafton, J., & Ratwani, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01586-2 · Cited by 35
“Fourth, providers may have succumbed to automation bias 23 , the tendency to over-rely on automation, which can be exacerbated under increasedworkload 24 .”
Ethics of artificial intelligence in prenatal and pediatric genomic medicine
Coghlan, S., Gyngell, C., & Vears, D. F. (2023) · Journal of Community Genetics · doi:10.1007/s12687-023-00678-4 · Cited by 39
“There is an ongoing risk here of AI conformity and automation bias—a recognized phenomenon in which humans unconsciously place excessive trust in computers and algorithms (Goddard et al. 2012).”
BackgroundAdversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans
Vadillo, J., Santana, R., & Lozano, J. A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1567 · Cited by 42
“In such cases, we can induce a human error confirmation attack by forcing the model to confirm the (wrong) medical diagnosis produced by the expert, that is, forcing f ( x ) = h ( x ) ≠ y x (Bortsova et al. 2021; Finlayson et al. 2019; Goddard, Roudsari, and Wyatt 2012; Johnson 2019).”
The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
Branda, F., Stella, M., Ceccarelli, C., Cabitza, F., Ceccarelli, G., Maruotti, A., Ciccozzi, M., & Scarpa, F. (2025) · Future Internet · doi:10.3390/fi17040145 · Cited by 40
“Research is needed to develop frameworks for determining accountability when AI chatbots cause harm and to explore how users’ over-reliance on AI affects their health outcomes [137].”
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
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“The lack of interest in the explanations provided by iCoRA might suggest some sort of automation complacency [29] in the students regarding iCoRA’s performance predictions.”
SimanTov‐Nachlieli, I. (2024) · Organization Science · doi:10.1287/orsc.2023.17515 · Cited by 32
Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography
Kim, S. H., Schramm, S., Riedel, E. O., Schmitzer, L., Rosenkranz, E., Kertels, O., Bodden, J. H., Paprottka, K. J., Sepp, D., Renz, M., et al. (2025) · La radiologia medica · doi:10.1007/s11547-025-01964-6 · Cited by 36
“One such phenomenon that has been described in the context of AI-assisted diagnosis is automation bias, which is the tendency of humans to overly rely on automated decision-making systems [19–23].”
Impact of AI recommendation correctness on diagnostic accuracy in clinical decision-making
Kücking, F., Busch, D., Przysucha, M., Kutza, J.-O., Hannemann, N., Hüsers, J., Babitsch, B., & Hübner, U. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106223 · Cited by 37
Gerlach, J., Hoppe, P. E., Jagels, S., Licker, L., & Breitner, M. H. (2022) · Electronic Markets · doi:10.1007/s12525-022-00603-6 · Cited by 35
“This type of bias arises because humans often tend to accept the recommendations of decision support systems without critically questioning them (Goddard et al., 2012).”
BackgroundFifty million people use computerised self triage
Wyatt, J. C. (2015) · BMJ · doi:10.1136/bmj.h3727 · Cited by 33
Self-citationTrustworthy Embodied Conversational Agents for Healthcare
Lupetti, M. L., Hagens, E., van der Maden, W., Steegers‐Theunissen, R. P. M., & Rousian, M. (2023) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3571884.3597128 · Cited by 33
“…for automation bias — the human tendency to over-accept computer output as a heuristic replacement of vigilant information-seeking and processing [19] (the phenomenon leading to the issue of overtrust discussed above)— that has shown to be even more prominent when it comes to AI systems [21].”
BackgroundBridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector
Fischer-Abaigar, U., Kern, C. A., Barda, N., & Kreuter, F. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2024.101976 · Cited by 64
Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis
Wang, G., Zhang, K., Jiang, J., Wang, C., Bi, H., Liang, H., Qi, Z., Huang, Y., Li, Y., & Yang, X. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02382-2 · Cited by 48
“Dedicated Human-Computer Interaction studies are needed to design interfaces that support traceability and mitigate cognitive biases 67 .”
From Human Driving to Automated Driving: What Do We Know About Drivers?
Xie, S., Chen, S., Zheng, J., Tomizuka, M., Zheng, N., & Wang, J. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3084149 · Cited by 32
Technology as Infrastructure for Dehumanization:
Oviatt, S. L. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3482855 · Cited by 35
“It is dangerous that people have inflated expectations and trust in technology, causing them to delegate authority to it in inappropriate ways and at inappropriate times [34; 42; 107].”
BackgroundIntelligent Clinical Decision Support
Pinsky, M. R., Dubrawski, A. W., & Clermont, G. (2022) · Sensors · doi:10.3390/s22041408 · Cited by 36
Cabilan, C. J., Hughes, J. A., & Shannon, C. (2017) · Journal of Clinical Nursing · doi:10.1111/jocn.13760 · Cited by 31
“As well, overreliance on technology can occur that is characterised by misplaced trust on the system’s clinical decision support than their own judgment (Goddard et al. 2012).”
BackgroundHolm, S., Stanton, C., & Bartlett, B. (2021) · Health Care Analysis · doi:10.1007/s10728-021-00430-4 · Cited by 32
Sakamoto, T., Harada, Y., & Shimizu, T. (2024) · JMIR Formative Research · doi:10.2196/58666 · Cited by 36
Rinta-Kahila, T., Penttinen, E., Salovaara, A., & Soliman, W. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.654 · Cited by 30
“, [1, 10, 23]) generally indicate that relying on automation may contribute to deskilling of employees as they gradually lose the essential process knowledge.”
BackgroundLearning to Revise References for Faithful Summarization
Adams, G., Shing, H.-C., Sun, Q., Winestock, C., McKeown, K. R., & Elhadad, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.296 · Cited by 34
Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making
Sele, D., & Chugunova, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298037 · Cited by 30
Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
Balagopalan, A., Baldini, I., Celi, L. A., Gichoya, J. W., McCoy, L. G., Naumann, T., Shalit, U., van der Schaar, M., & Wagstaff, K. L. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000474 · Cited by 34
“Well-characterized issues include automation bias [112–114] (overreliance on machine recommendations), algorithm aversion [115,116] (underreliance on machine recommendations), and alarm fatigue [117] (becoming overwhelmed by the frequency of alerts).”
BackgroundQazi, I. A., Ali, A., Khawaja, A. U., Akhtar, M. J., Sheikh, A. Z., & Alizai, M. H. (2026) · NEJM AI · doi:10.1056/aioa2501001 · Cited by 37
Starke, S. D., & Baber, C. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2018.01.010 · Cited by 29
Snow, T. (2021) · Data & Policy · doi:10.1017/dap.2020.25 · Cited by 33
“Automation bias manifests in both errors of commission (following incorrect algorithmic advice) and omission (failing to act because the algorithmic tool does not prompt the practitioner to do so) (Goddard et al., 2012).”
BackgroundDekker, R., Koot, P., Birbil, Ş. İ., & Andres, M. V. E. (2022) · Big Data & Society · doi:10.1177/20539517221087855 · Cited by 29
“Public sector decision-makers, as all human beings, generally tend to favor the results presented by machines, even if these are mistaken — a phenomenon known as automation bias (Bullock et al., 2020; Goddard et al., 2012; Yeung, 2017).”
We and It: An Interdisciplinary Review of the Experimental Evidence on Human-Machine InteractionPreprint
Chugunova, M., & Sele, D. (2020) · SSRN Electronic Journal · doi:10.3929/ethz-b-000442053 · Cited by 30
Exploring the next generation of international entrepreneurship
Chakravarty, S., Cumming, D. J., Murtinu, S., Scalera, V. G., & Schwens, C. (2021) · Journal of World Business · doi:10.1016/j.jwb.2021.101229 · Cited by 31
Artificial intelligence and deskilling in medicine
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E. D., Bauer, R., & Bauer, M. (2026) · The British Journal of Psychiatry · doi:10.1192/bjp.2025.10496 · Cited by 26
Li, J., Zhou, L., Zhan, Y., Xu, H., Zhang, C., Shan, F., & Liu, L. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac179 · Cited by 28
Brejnebøl, M. W., Lenskjold, A., Ziegeler, K., Ruitenbeek, H., Müller, F. C., Nybing, J. D., Visser, J. J., Schiphouwer, L. M., Jasper, J., Bashian, B., et al. (2024) · Radiology · doi:10.1148/radiol.233341 · Cited by 28
The Ethics of Artificial Intelligence in Defence
Taddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.001.0001 · Cited by 30
Preventing Racial Bias in Federal AI
Livingston, M. (2020) · Journal of Science Policy & Governance · doi:10.38126/jspg160205 · Cited by 31
“Even when staff review the AI prediction prior to any action being taken, the staff may overlook errors due to ‘ automation bias, ’ the human tendency to place too much trust in automated decisions in spite of contradicting evidence (Goddard, et al. 2012).”
BackgroundResultSelf-fulfilling prophecies and machine learning in resuscitation science
De-Arteaga, M., & Elmer, J. (2023) · Resuscitation · doi:10.1016/j.resuscitation.2022.10.014 · Cited by 28
Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in HealthcarePreprint
Zack, T. I., Lehman, E., Süzgün, M., Rodriguez, J. A., Celi, L. A., Gichoya, J. W., Jurafsky, D., Peter Szolovits, B., Bates, D. W., Abdulnour, R.-E. E., et al. (2023) · medRxiv · doi:10.1101/2023.07.13.23292577 · Cited by 28
Human-artificial intelligence interaction in gastrointestinal endoscopy
Campion, J. R., O’Connor, D. B., & Lahiff, C. J. (2024) · World Journal of Gastrointestinal Endoscopy · doi:10.4253/wjge.v16.i3.126 · Cited by 31
“The potential for negative outcomes due to automation bias has been explored through a human factors paradigm in healthcare and other settings requiring high levels of accuracy[50,52].”
Düvel, J. A., Lampe, D., Kirchner, M., Elkenkamp, S., Cimiano, P., Düsing, C., Marchi, H., Schmiegel, S., Fuchs, C., Claßen, S., et al. (2024) · JMIR Human Factors · doi:10.2196/66699 · Cited by 30
“For example, automation bias, in which health care providers may develop an overreliance on AI recommendations, potentially reducing their clinical vigilance and leading to oversights in patient care, needs to be considered [46].”
Minimizing bias when using artificial intelligence in critical care medicine
Ranard, B. L., Park, S., Jia, Y., Zhang, Y., Alwan, F. S., Celi, L. A., & Lusczek, E. R. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154796 · Cited by 27
Spectrum of Cognitive Biases in Diagnostic Radiology
Yoon, S.-Y., Lee, K. S., Bezuidenhout, A. F., & Kruskal, J. B. (2024) · Radiographics · doi:10.1148/rg.230059 · Cited by 26
The effects of generative AI’s human-like competencies on clinical decision-making
Spatscheck, N., Schaschek, M., & Winkelmann, A. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2430731 · Cited by 28
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
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