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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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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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Feldman, E., & Cremer, D. D. (2024) · AI and Ethics · doi:10.1007/s43681-024-00602-7 · Cited by 4
“Deferring to computer decisions might lead to doctors implicitly and subconsciously disengaging from a patient’s clinical course [12].”
The Prospect of Artificial Intelligence‐Supported Ethics Review
Nickel, P. J. (2024) · Ethics & Human Research · doi:10.1002/eahr.500230 · Cited by 6
Colbert, C. M., Melancon, D., Kang, J., Ford, E. C., & Smith, W. P. (2025) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2025.04.009 · Cited by 4
Yigamawano, F. K., Odom-Mabey, A. R., Xue, C., Pandey, H. K., & Kolachalama, V. B. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100396 · Cited by 4
Jiang, H., & Liu, Y. (2026) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2026.102216 · Cited by 4
Derechos y responsabilidades de los usuarios de una historia clínica electrónica
Sittig, D. F. (2013) · Archivos Argentinos de Pediatria · doi:10.5546/aap.2013.469 · Cited by 4
Hevelius Report: Visualizing Web-Based Mobility Test Data For Clinical Decision and Learning Support
Lin, H., Han, T., Gajos, K. Z., & Gupta, A. S. (2024) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663548.3688490 · Cited by 4
“Designing to meet human experts’ diverse needs in clinical tasks is crucial for reducing over-reliance on automation and the resulting high-stake erroneous decisions [12].”
Guyton, Z., Pak, R., & Rovira, E. M. (2024) · Applied Ergonomics · doi:10.1016/j.apergo.2024.104430 · Cited by 5
Emotion-aware Human–Robot Interaction and Social Robots
Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398.3502407 · Cited by 4
Buhr, L., & Schicktanz, S. (2022) · Big Data & Society · doi:10.1177/20539517221092653 · Cited by 4
“…as ethical questions of reliability, explainability, opacity, paternalism, automation bias, trust-worthiness, the potential traceability of supposedly anonymous data, fairness and moral responsibility (Floridi et al., 2018; Goddard et al., 2012; Grote and Berens, 2020; Mittelstadt et al., 2016).”
BackgroundIch weiß, was du nächsten Sommer getan haben wirst
Adensamer, A., & Klausner, L. D. (2019) · Zeitschrift für kritik - recht - gesellschaft · doi:10.33196/juridikum201903041901 · Cited by 4
An Empirical Investigation of Reliance on AI-Assistance in a Noisy-Image Classification Task
Tejeda, H., Kumar, A., & Mark Steyvers, B. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220201 · Cited by 4
The Societal Impacts of Algorithmic Decision-Making
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195 · Cited by 8
Prediction Power as a Challenge for the Rule of Law
Ruschemeier, H. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198877820.013.50 · Cited by 6
Brauner, P., Schaar, A. K., & Ziefle, M. (2022) · Springer eBooks · doi:10.1007/978-3-030-99235-4_14 · Cited by 4
Furniss, D. J., Habli, I., Sujan, M. A., & Hawkins, R. (2020) · Safety-Critical Systems Symposium (SSS) · doi:10.65391/r1186 · Cited by 4
“While studies have suggested a number of strategies such as explainability and transparency of decision making, clear accountability and adaptive interfaces and task allocation, the evidence base for these is far from clear (Goddard et al., 2012).”
BackgroundInternational Dimensions of Clinical Decision Support
Fraser, H., & Wyatt, J. C. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-398476-0.00008-7 · Cited by 4
Self-citationThe Influence of Cultural Factors on Trust in Automation
Chien, S.-Y. J., Lewis, M., Sycara, K., Liu, J.-S., & Kumru, A. (2013) · D-Scholarship@Pitt (University of Pittsburgh) · doi:10.3886/e100532v1 · Cited by 4
U.S. Military Investments in Autonomy and AI: A Strategic Assessment
Konaev, M., Chahal, H., Fedasiuk, R., Huang, T., & Rahkovsky, I. M. (2020) · doi:10.51593/20190044 · Cited by 4
Bayesian Logical Neural Networks for Human-Centered Applications in MedicinePreprint
Ochoa, J. G. D., Maier, L., & Csiszár, O. (2021) · medRxiv · doi:10.1101/2021.11.15.21266351 · Cited by 4
Johnson, D. K. N. (2025) · The Review of Black Political Economy · doi:10.1177/00346446251331615 · Cited by 3
“While machines all have some degree of dependence on humans while operating, depending upon the speed of decision-making (e.g., real-time responses to queries on Google versus decisions on mortgage applications), depending upon the frequency of interaction with the operator, and depending upon the ability of the algor…”
Humans Follow AI-Advice by (un-)trustworthy Virtual Agents in Ethical Healthcare Decisions
Dübeler, D., Schütze, C., Müller, A., Richter, B., & Wrede, B. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94171-9_24 · Cited by 3
Artificial Intelligence in Mental Healthcare
Jafar, A., Shabnam, S., & Khan, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5072-1.ch014 · Cited by 3
Ba, S., Lu, G., Mendoza, N. B., Yang, Y. P., Pan, Z., & Wang, Y. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251396357 · Cited by 3
“This observation aligns with the concept of automation bias (Goddard et al., 2012), which suggests that self-driven learners might unintentionally grant excessive authority to the chatbot ’ s responses, potentially missing opportunities for deeper interrogation and synthesis.”
Krzywdzinski, M., Wotschack, P., Gonnermann-Müller, J., & Gronau, N. (2025) · AI & Society · doi:10.1007/s00146-025-02761-5 · Cited by 7
Fabris, A., Rus, C., Saldivar, J., Gatzioura, A., Biega, A. J., & Castillo, C. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104506 · Cited by 3
Royal College of Radiologists Guidance Statements on the Use of Auto-contouring in Radiotherapy
Mackay, K., Banfill, K., Bernstein, D., Daniel, J., Díez, P., Gwynne, S., Hoole, A. C. F., Jena, R., Marchant, T., Nix, M. G., et al. (2025) · Clinical Oncology · doi:10.1016/j.clon.2025.104004 · Cited by 3
Zand, J., Šuvakov, M., Overgaard, S., Kozikowski, C., Jimison, R. C., Nelson, K. B., Silvano, C. J., Meiners, L. M., Fan, J. W., Schultz, K. E., et al. (2026) · Mayo Clinic proceedings · doi:10.1016/j.mayocp.2025.11.008 · Cited by 3
Automation bias and overconfidence in artificial intelligence and associated legal implications
Mezrich, J. L. (2026) · Clinical Imaging · doi:10.1016/j.clinimag.2026.110745 · Cited by 3
Remembering with AI: From Distributed Memory to AI-Curated and Human-AI Co-Memory
Telakivi, P. (2026) · Review of Philosophy and Psychology · doi:10.1007/s13164-026-00815-1 · Cited by 5
The coalescent architecture of agency: normative directionality as the key to human–AI integration
Torres–Martínez, S. (2026) · AI & Society · doi:10.1007/s00146-026-02979-x · Cited by 3
“Recent systematic reviews confirm that automation bias manifests in two distinct error types: errors of commission (following incorrect automated advice) and errors of omission (failing to notice problems when automated systems do not flag them) (Goddard et al. 2012; Lyell and Coiera 2017).”
Perrella, A., Micco, P. D., Trama, U., di Silverio, P., Maffettone, A., Piccinocchi, G., & Bernardi, F. F. (2026) · Healthcare · doi:10.3390/healthcare14070847 · Cited by 3
“The 32.6% of GPs who understand benefits but not limitations appear particularly vulnerable to automation bias [14].”
InfluentialEvaluation of Human Factors–Related Risks in AI-Enabled Medical Devices — A Practical Guide
Mathias, R., Schmitt, A., Campos, M., Vasey, B., Lorenz, S., McCulloch, P. G., & Gilbert, S. (2026) · NEJM AI · doi:10.1056/aipc2501297 · Cited by 4
How do automation false alarms and misses affect human trust in automated driving?
Li, J., Liu, J., & Liu, L. (2026) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2026.103939 · Cited by 3
Yu, X., He, L., Qin, J., & Liu, T. (2026) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2026.103942 · Cited by 3
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Jovchevski, P., Buijsman, S., & Neerincx, M. A. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01090-9 · Cited by 4
Wi, C.-I., Overgaard, S. M., Malik, M., Watson, D., Sharma, D., Le‐Rademacher, J. G., Kelleher, D. P., Zheng, L., Ohde, J. W., Lynch, B., et al. (2026) · Journal of the National Medical Association · doi:10.1016/j.jnma.2026.04.001 · Cited by 5
Sunwoo, L., Ryu, W.-S., Buch, K. A., Conklin, J., Mehan, W. A., Desalvo, M. N., Loken, E. K., Rincon, S., Rocha, T., Succi, M. D., et al. (2026) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2026-025339 · Cited by 3
“A complementary reliance level BHR was computed from the Goddard framework 20 as positive consultations divided by negative consultations.”
Artificial Intelligence in Image Assisted Radiation Oncology
Wang, H., Zhao, Y., Chen, X., McDonald, B. A., Li, Y., Xie, J., Rhee, D. J., Lim, T. Y., Netherton, T., Phan, J., et al. (2026) · Cancers · doi:10.3390/cancers18111715 · Cited by 3
“Critical concerns include algorithmic bias [259], which occurs when AI models exhibit systematic disparities in performance due to unrepresentative training data or flawed model design, and automation bias, where clinicians may over-rely on machine-generated decision-making [259,260].”
Dominitz, J. A., Gawron, A. J., McKee, G., Hoggatt, K. J., & Kaltenbach, T. R. (2026) · Gastroenterology · doi:10.1053/j.gastro.2026.05.018 · Cited by 3
Liu, C.-H., Chen, Y.-T., Hsu, C., Chen, H.-L., & Yang, C.-W. (2026) · JMIR Medical Education · doi:10.2196/94951 · Cited by 3
“A central educational concern in AI integration is automation bias, the tendency to overrely on automated outputs without adequate scrutiny [27].”
Wójcik, S., Rulkiewicz, A., & Domienik‐Karłowicz, J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1933451 · Cited by 3
ARTIFICIAL INTELLIGENCE (AI) BIAS IMPACTS: CLASSIFICATION FRAMEWORK FOR EFFECTIVE MITIGATION
(2023) · Issues in Information Systems · doi:10.48009/4_iis_2023_129 · Cited by 4
WHERE: Humans, Technology, and Humane Technology
Walther, C. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-67823-3_2 · Cited by 4
Regulatory and Legal Considerations with Artificial Intelligence in Dermatology
Shah, N., Mourby, M. J., & Matin, R. (2025) · Dermatologic Clinics · doi:10.1016/j.det.2025.05.011 · Cited by 3
Whose Wisdom? Human Biases for Decision Support System Source and Scale
Miller, C. M., Patton, C. E., Escobedo, A. R., & Guzman-Bonilla, E. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251360707 · Cited by 3
“Additionally, contrary to our hypothesis (H1) and earlier reports of automation bias (Goddard et al., 2012; Madhavan & Wiegmann, 2007), participants showed no overall prefer‑ ence for ML over human advice when size was held con‑ stant.”
Li, K. Y., Huang, K., Harmer, B. M., Ducharme, C., Heasman, B., Falahee, E., Cooke, J., Cole, M., Sample, A. P., & Popov, V. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3768164 · Cited by 3
“This is particularly problematic in simulation training where instructors may develop “automation bias” [36], emphasizing the need for balanced trust and doubt.”
Lyell, D., Dinh, M. M. Q., Gillett, M., Abraham, N., Symes, E. R., Susanto, A. P., Chakar, B., Seimon, R. V., Coiera, E. W., & Magrabi, F. (2025) · Emergency Medicine Journal · doi:10.1136/emermed-2024-214781 · Cited by 3
“The risk of incorrect decision support biasing doctors and potentially leading to misdiagnoses, a phenomenon known as automation bias, has long been of concern 24 25 and has been observed in computer-aided interpretation of electrocardiograms 26 and screening mammograms.”
Sittig, D. F., Longhurst, C., Russo, E. M., & Singh, H. (2015) · Health informatics · doi:10.1007/978-3-319-20765-0_2 · Cited by 3
Meyer, J., Dembinsky, O., & Raviv, T. (2020) · Computers & security · doi:10.1016/j.cose.2020.101944 · Cited by 2
On the Moral Hazard of Autonomy
Morris, A., Maddalon, J. M., & Miner, P. S. (2020) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc50938.2020.9256682 · Cited by 3
“Automation complacency is defined as insufficient attention to monitor automation output because the output is viewed as reliable [30].”
BackgroundOver‐reliance on database: A case study of using web of science
Lou, Y., & Sun, Q. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.266 · Cited by 3
“Automation bias and automation complacency have been widely used to explain over-reliance on technology (Coiera, 2015; Goddard et al., 2012; Parasuraman & Manzey, 2010; Skitka et al., 1999).”
Method
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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