ArticleJAMIA open2024
Perceptions and attitudes toward artificial intelligence among frontline physicians and physicians' assistants in Kansas: a cross-sectional survey.
Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model Integrating Trust and Risk.Healthcare (Basel, Switzerland) · 2026Article
- Perceptions of artificial intelligence among pulmonologists.ERJ open research · 2026Article
- When self-control is no longer a protective factor: the moderating effect of institutionalized identity on attitudes toward artificial intelligence.Frontiers in psychology · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- Public perceptions of AI in healthcare: a large-scale BERTopic and sentiment analysis of Reddit discussions.Frontiers in public health · 2026Article
- Physicians' Perceptions of AI and Extended Reality in Telemedicine: A Multi-Specialty Cross-Sectional Survey in Romania.Healthcare (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Triadic relations in healthcare: surveying physicians' perspectives on generative AI integration and its role on empathy, the placebo effect and patient care.Frontiers in psychology · 2025Article
- Identifying Measurement Dimensions of Users' Benefit-Risk Perceptions of AI in Healthcare: A Scoping Review.Inquiry : a journal of medical care organization, provision and financingArticle
- AI support in prehospital telemedicine: Perspectives of tele-emergency physicians and ethical considerations.Digital healthArticle
- Article
- Artificial Intelligence in Healthcare: Awareness, Perceptions, and Future Perspectives of Palestinian Medical Students and Physicians.Journal of medical education and curricular developmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This survey aims to understand frontline healthcare professionals' perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions. Materials and Methods: We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants' familiarity and experience with AI on the survey results. Results: From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits ( Discussion: The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals' intention to use AI, regardless of their AI familiarity. Conclusion: The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals' perspective. These insights can guide strategies for successful AI implementation in healthcare.
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.