ArticleOxford open digital health2026
Healthcare professionals' perspectives on artificial intelligence in clinical practice: a systematic review of facilitators and challenges.
Article in Oxford open digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Artificial Intelligence Adoption in Healthcare Practice and Research: A Cross-Sectional Study of Knowledge, Attitudes, and Practices in Balochistan, Pakistan.Health science reports · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) tools enhance health care by decision making, reducing errors, and delivering the best care to patients. Healthcare professionals are the users of the AI tools, and it is essential to have knowledge and skills in the utilization of AI tools to deliver effective care. Therefore, this study systematically explores the healthcare professional perspectives in using AI tools in clinical practice and identifies the facilitators and challenges associated with their usage. Three databases, PubMed, EBSCOhost, and ACM Digital Library, are systematically used to identify qualitative research studies. Appropriate selection of research articles is carried out by the inclusion and exclusion criteria. The Critical Appraisal Studies Programme tool is utilized for assessment. Data are extracted and analysed effectively. Out of 1292 articles,10 qualitative research studies which meet the objectives of the research are included. By analysing the information, six themes developed are behaviour, perceived usefulness, performance expectancy, ethical and legal aspects, challenges, and AI tool proficiency. Healthcare professionals acknowledge the value of AI tools in clinical practice; however, clinicians often lack the necessary competencies for their effective deployment. It is therefore imperative that healthcare practitioners collaborate with developers during the design phase of AI systems, ensuring due consideration of ethical and legal requirements. While AI technologies offer numerous advantages, prioritizing transparency and explainability is essential to optimize their integration within clinical workflows. Ongoing proficiency with AI tools may be sustained through structured training programmes and the establishment of clear operational guidelines.
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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.