ArticleDigital health
Overcoming barriers and enabling artificial intelligence adoption in allied health clinical practice: A qualitative 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 22 papers, 1 of them a synthesis that pooled it.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in real-world clinical workflows: a systematic review of applications and implementation.Frontiers in digital health · 2025Pooled it
- Acceptance and Use of Generative AI in Clinical Rehabilitation: An Interdisciplinary Survey of US Rehabilitation Professionals.Archives of rehabilitation research and clinical translation · 2026Article
- The Challenges of AI Applications in Health Care: Qualitative Study.Journal of medical Internet research · 2026Article
- An exploratory survey of barriers and enablers of AI adoption in rural and urban dental practices in Mecklenburg-Western Pomerania, Germany.Scientific reports · 2026Article
- Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Article
- Article
- Challenges in accessing and implementing innovative interventions in occupational therapy.BMC health services research · 2026Article
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- Design and application of a web-based intelligent ophthalmic image analysis teaching platform.Frontiers in medicine · 2026Article
- Understanding AI adoption through expert discourse: A UTAUT-based analysis on LinkedIn.PloS one · 2026Article
- AI literacy and attitudes among maternal and child health nurses: a multicenter psychological network analysis of novices and experts in China.Frontiers in public health · 2026Article
- What Are the Opportunities and Challenges of Using AI in Medical Education in Vietnam?JMIR medical education · 2025Article
- Factors associated with intention to use generative artificial intelligence in nursing practice: a cross-sectional study.BMC nursing · 2025Article
- Quo Vadis, AI-Empowered Doctor?JMIR medical education · 2025Article
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Article
- Building AI competence in the healthcare workforce with the AI for clinical care workshop: A Bridge2AI for clinical CHoRUS project.Journal of clinical and translational science · 2025Article
- The Utilization of Artificial Intelligence by Pediatric Otolaryngology Surgeons in Professional Practice.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-facialeArticle
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
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has the potential to revolutionise healthcare. If the implementation is successful it has the potential to improve healthcare outcomes for patients and organisations. Little is known about the perceptions of allied health professionals (AHPs) towards AI in healthcare. Objective: This study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective. Methods: Qualitative methodology informed by behaviour change theory using focus groups with AHPs at a health service in Queensland, Australia. Results: Twenty-four barriers and 24 enablers were identified by 25 participants across four focus groups. Barriers included: lack of AI knowledge, explainability challenges, risk to professional practice, negative impact on professional practice, and role replacement. Enablers include AI training and education, regulation, reputation, understanding the healthcare benefits of AI and engaging clinical champions. Conclusions: AHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use. Organisations must take a proactive approach and adopt targeted and multifaceted strategies to address barriers. This may include workforce upskilling, clear communication of the benefits of AI use of local champions and ongoing research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.