ReviewJournal of healthcare leadership2026
Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications - A Systematic Review.
Review in Journal of healthcare leadership, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Generative artificial intelligence (GenAI) is rapidly transforming eHealthcare, promising substantial gains in diagnostic accuracy, workflow efficiency, and decision support, yet raising concerns about automation bias and clinician deskilling that may erode core diagnostic expertise and professional judgment over time. Aim and Objectives: This systematic review investigates the dual impact of GenAI in eHealthcare, focusing on how it enhances clinical efficiency and decision support while potentially diminishing clinicians' diagnostic expertise and professional judgment. Methods: A systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. A total of 11,269 records were identified, and 29 studies met inclusion criteria following PRISMA 2020 guidelines. Studies were synthesized using a narrative approach due to heterogeneity in design, clinical domains, and AI system maturity. Results: Across the included studies, recurrent themes included automation bias (reported in 10 studies), concerns regarding clinician deskilling (9 studies), and impacts on diagnostic reasoning (9 studies). Evidence was predominantly observational, experimental simulation-based, or conceptual in nature. Findings reveal that GenAI significantly improves diagnostic accuracy, workflow efficiency, and decision quality across radiology, telehealth, and education domains. However, overreliance introduces risks of automation bias, cognitive deskilling, and loss of interpretive autonomy. The review identifies key mitigation strategies, including human-in-the-loop frameworks, explainable AI (XAI), ethical governance, and continuous clinician reskilling. Theoretically, the results redefine the clinician-AI relationship as a dynamic cognitive partnership, while practically they emphasize responsible integration through education and regulation. Conclusion: Sustainable adoption of GenAI demands balanced implementation-leveraging its analytical capabilities without compromising human judgment, clinical reasoning, or professional accountability.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.