Allied health professional attitudes toward artificial intelligence in healthcare: A UK cross-sectional survey

Objectives: Allied Health Professionals (AHPs) represent the second largest healthcare workforce in the United Kingdom, yet their perspectives on artificial intelligence (AI) remain underrepresented in the literature. As national policy commits to making the NHS “the most AI-enabled care system in the world”, understanding AHP engagement with AI is essential for effective workforce development and implementation. Methods: An anonymous cross-sectional survey was conducted among AHPs attending a 2025 regional Research and Innovation Conference in the United Kingdom. The survey explored demographics, AI familiarity and use, perceived benefits and concerns, and views on regulation. Data were analysed using descriptive statistics, Kruskal–Wallis and chi-square tests, and exploratory binary logistic regression, following STROBE and CHERRIES guidelines. Results: Among 162 respondents from nine AHP professions, 54.9% reported personal use of large language models, while only 21.7% used AI professionally, mainly for administrative tasks. Healthcare-specific AI familiarity was low (median = 2/5). In exploratory regression analysis familiarity with healthcare AI emerged as the only significant predictor of comfort with AI-supported clinical decision-making (OR = 2.28, 95% CI [1.49–3.47], p < .001); age, personal AI use, and discipline were not independent predictors. Radiographers demonstrated greater healthcare AI familiarity than dietitians (p < .001) and occupational therapists (p = .021). Most participants viewed AI as an assistive tool requiring human oversight (93.2% wanted override ability), and fewer than 20% were comfortable with AI making patient-care decisions. Access to education and training was identified as a key enabler by 90.1% of respondents. Conclusions: Workplace integration of AI remains limited despite increasing accessibility of AI technologies. Comfort with clinical AI use was associated with familiarity with healthcare-specific applications and awareness of their potential uses. These findings suggest implementation efforts may benefit from improving foundational understanding of AI and its applications, alongside clear accountability frameworks.