ArticleBMC health services research2026
Understanding psychiatrist readiness for AI: a study of access, self-efficacy, trust, and design expectations.
Article in BMC health services research, 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.
- Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) is receiving growing attention in psychiatric practice, yet psychiatrists vary considerably in how they perceive its benefits, risks, and clinical usefulness. Successful implementation depends not only on technological performance but also on clinicians' readiness, including their access to AI, confidence in using it, trust in its reliability, and expectations for its design. Evidence on these dimensions remains limited in China.
objectiveThis study examined Chinese psychiatrists' readiness for AI across four dimensions-access, self-efficacy, trust, and design expectations-and explored variation across demographic and professional subgroups.
methodA cross-sectional online survey was distributed through the WeChat platform from March 20 to 22, 2025. Eligible participants were licensed psychiatrists engaged in clinical practice. A total of 134 valid responses were obtained from clinicians across diverse provinces, hospital tiers, and professional roles. Descriptive analyses and group comparisons were conducted.
resultsPsychiatrists reported broad exposure to AI, though knowledge was largely acquired through informal rather than structured sources. Overall self-efficacy was moderate, with higher confidence observed among younger clinicians, male clinicians, and those who had received AI-related training. Trust in AI was generally positive, and department heads expressed especially strong confidence in its future role. Across subgroups, psychiatrists consistently prioritized AI applications that reduce administrative and documentation burden, while expressing lower expectations for AI use in communication, assessment, or psychotherapy.
conclusionChinese psychiatrists demonstrated cautious optimism and moderate readiness for AI. Limited access to formal training and subgroup differences in confidence highlight the need for targeted capacity-building. The strong preference for documentation-support tools underscores the importance of designing AI systems that integrate smoothly into clinical workflows while preserving the human-centered nature of psychiatric care.
Indexed as
Identifiers
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.