ArticleDigital health
Perceptions, barriers, and risk of artificial intelligence Among healthcare professionals: A cross-sectional study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Patient attitudes toward artificial intelligence in Jordanian Healthcare: A cross-sectional survey study.PLOS digital health · 2026Article
- Healthcare Professionals' Perceptions of AI-Assisted Clinical Decision-Making in Jordan: A Qualitative Study of Trust, Accountability, System Readiness, and Professional Practice.Healthcare (Basel, Switzerland) · 2026Article
- Exploring Students' Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar.Healthcare (Basel, Switzerland) · 2026Article
- Assessing Primary Care Physicians' Readiness for AI-Based Adaptive Learning: Perceptions, Barriers, and Learning Needs in Northern Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Measuring Positive and Negative Attitudes Associated With the Use of Artificial Intelligence in Nursing Profession: Cross-Cultural Adaptation and Psychometric Analysis of ASUAITIN.Journal of nursing management · 2026Article
- Perception and Knowledge About Telenursing and eHealth Among Nursing University Students: A Cross-Sectional Study in the United Arab Emirates.Public health nursing (Boston, Mass.)Review
- Knowledge and attitudes towards artificial intelligence use in healthcare among the general public in Jordan.Digital healthArticle
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
Purpose: This study found that healthcare professionals expressed diverse opinions regarding the integration of AI in the Jordanian healthcare sector. This study aimed to explore healthcare professionals' perceptions of artificial intelligence (AI) and how these perceptions relate to perceived barriers and potential risks associated with AI integration in clinical practice in Jordan. Materials and Methods: A cross-sectional survey of 605 healthcare professionals who were conveniently selected from four types of hospitals, namely private, teaching-affiliated, Royal Medical Service, and public in Amman city and the northern regions of Jordan. Results: About half of the respondents were female (50.7%), holding a bachelor's degree (60.5%) and having 1-5 years of experience (33.1%), working in the Ministry of Health (47.4%). This study found that healthcare professionals hold optimistic perceptions of AI integration in the Jordanian healthcare sector. There is a moderate level of perceived barriers and a perceived level of risks associated with AI incorporation into the healthcare sector. Conclusion: This study highlights the need for more transparent policies, better communication, and more robust frameworks for 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.