Research and Scholarly Methods: Using Artificial Intelligence
Clinical Snapshot
PICO Framework
| P — Population | Pharmacy researchers and clinical pharmacists engaged in health research |
| I — Intervention | Integration of artificial intelligence tools (machine learning models, large language models) into pharmacy research workflows |
| C — Comparator | Traditional research methods without AI augmentation |
| O — Outcomes | Guidance on appropriate AI use cases, tool selection, risk mitigation, reporting standards, and optimisation of scientific discovery and patient outcomes |
Bottom Line
This narrative review from the University of Colorado provides a practical orientation for pharmacy researchers seeking to integrate AI tools — including machine learning models and large language models — into their research practice. The paper is well-intentioned and addresses a genuine methodological gap, offering guidance on use case identification, tool selection, risk mitigation, and reporting standards. However, as a narrative review without a systematic search strategy, it carries inherent selection and enthusiasm bias risks and cannot be considered a rigorous evidence synthesis. No original data are presented, and no quantitative estimates of benefit are provided. The recommendations are largely expert opinion-level evidence (CEBM Level 5). For Australian pharmacy researchers, the guidance requires contextual adaptation to TGA regulatory requirements, NHMRC research integrity standards, and Australian data governance frameworks. Clinicians and researchers should treat this paper as a useful conceptual starting point rather than a definitive evidence-based protocol. Institutional AI governance policies, ethics board requirements, and validated reporting frameworks (such as CONSORT-AI or TRIPOD-AI) should be consulted alongside this paper before implementing AI in formal research programs.
Key Findings
P Value: Not reported — narrative review design
Effect Size: Not applicable — no primary empirical data generated
Primary Outcome: Narrative guidance framework for pharmacy researchers on identifying appropriate AI use cases, selecting tools, mitigating risks, and adhering to reporting standards in AI-assisted research
Nnt Or Sensitivity: Not applicable — methodological guidance paper; no diagnostic, therapeutic, or prognostic estimates generated
Confidence Interval: Not reported — narrative review design
Clinical Application
Conceptually feasible for adoption in academic and hospital pharmacy research settings. Practical implementation will depend heavily on institutional data infrastructure, ethics approval processes for AI use, staff training capacity, and access to validated AI platforms. Smaller institutions and community pharmacy settings may face significant resource barriers. Australian pharmacy researchers should interpret this guidance in the context of the Therapeutic Goods Administration (TGA) regulatory framework for AI-based medical software, the Australian Privacy Act 1988 and Health Records legislation governing research data use, and NHMRC guidelines on research integrity. The Pharmaceutical Benefits Scheme (PBS) generates rich real-world prescribing data that could be leveraged with AI methods, but access is governed by strict data custodianship requirements through the Australian Institute of Health and Welfare (AIHW) and Services Australia. RACGP and SHPA (Society of Hospital Pharmacists of Australia) have not yet issued formal guidance on AI use in pharmacy research as of mid-2025, making this paper a timely but US-framed reference point. Australian researchers should also note that TGA's Digital Health and AI regulatory pathway differs from FDA frameworks referenced implicitly in this paper. Pharmacy researchers, clinical pharmacists engaged in research, academic health professionals seeking to integrate AI tools into research workflows, and research governance committees evaluating AI use policies
Abstract
Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.
References
- 1.Trinkley, K. E., & Maw, A. M. (2026). Research and scholarly methods: Using artificial intelligence. Journal of the American College of Clinical Pharmacy: JACCP. Advance online publication. https://doi.org/10.1002/jac5.70239
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