ArticleBMJ health & care informatics2025
Artificial intelligence guided dosing decisions: a qualitative study on health care provider perspectives.
Article in BMJ health & care informatics, 2025. 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
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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
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Who cites it
2 citing papers in PubMed.
- Adaptive AI-based mobile simulation for drug calculation competency in nurses: a mixed-methods RCT.BMC nursing · 2026Article
- Knowledge, Attitudes, and Training Needs for AI in Primary Care: National Survey Study of Clinicians in the Veterans Health Administration.JMIR formative research · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTailoring medication dosing to an individual's traits is complex, but artificial intelligence (AI) advancements enable greater precision. Our study objectives were to gauge healthcare providers' perspectives on AI-guided precision dosing and to identify barriers and enablers for adopting AI-guided precision dosing into clinical practice.
methodsWe conducted a qualitative study using purposive sampling to select a diverse group of healthcare providers, thereby broadening the viewpoints. We explored their receptiveness to AI-enabled dosing and sought to uncover implementation challenges. During the interviews, we introduced CURATE.AI as an example of an AI dosing tool. We analysed the data using deductive methods, coding the data according to the Unified Theory of Acceptance and Use of Technology framework.
resultsWe interviewed 16 participants (9 doctors, 4 nurses and 3 pharmacists). Interviews revealed diverse perspectives, from hopeful anticipation to recognised challenges. While acknowledging AI's potential to enhance decision-making and patient safety, concerns about AI's suitability for complex cases, erosion of critical thinking, liability protection, and trust arose. Moreover, transparency, understandability of AI output and human oversight were seen as essential to mitigate risks and promote acceptance. DISCUSSION: AI-enabled dosing tools have the potential to optimise dosing and improve patient safety, but adoption barriers remain. Successful implementation will require technically robust tools and careful alignment with clinical workflows and user expectations.
conclusionOur study highlights the hopeful anticipation and complex challenges of introducing AI-enabled dosing into clinical practice. As AI inevitably becomes a part of healthcare, ongoing evaluation is essential to demonstrate value and promote adoption.
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