This summary is based on peer-reviewed literature and current clinical guidelines, cited below.
Background
Artificial intelligence — machine learning models, clinical decision support augmentation, and more recently large language models (LLMs) — is moving from pilot projects into routine pharmacy workflows: drug interaction screening, adverse drug event prediction, dispensing automation, and medication therapy management. The clinically important question for pharmacists isn’t whether AI is coming, but where it demonstrably helps, and where it still requires close human oversight.
Clinical Considerations
Where AI Is Already Applied in Pharmacy
A 2023 literature review identified AI applications spanning the medication-use process: predicting and detecting adverse drug events, augmenting clinical decision support for medication-related decisions, automating dispensing in community pharmacies, optimizing dosing, detecting drug-drug interactions, and supporting medication therapy management and adherence.3 The common thread across these applications is pattern recognition across large volumes of structured data (medical records, lab results, medication profiles) — tasks well-suited to machine learning, distinct from the open-ended clinical reasoning tasks now being tested with LLMs.3
“Augmented,” Not Autonomous, Intelligence
A primer published in the American Society of Health-System Pharmacists’ own journal frames AI in pharmacy as “augmented intelligence” rather than a replacement for clinical judgment — leveraging computational strengths (processing large datasets, consistency, speed) alongside clinician strengths (contextual judgment, communication, accountability).1 The paper is explicit that pharmacists, as medication-use domain experts, have a direct role in developing and evaluating these models — not simply adopting whatever a vendor ships — and it outlines core AI/ML vocabulary pharmacists need to meaningfully collaborate with data scientists on model development, validation, and maintenance.1
Where Generative AI Still Fails
A 2025 mixed-methods study benchmarked eight mainstream generative AI systems (including GPT-4o, Claude, Gemini, and DeepSeek-R1) against 48 clinically validated pharmacy questions across medication consultation, medication education, prescription review, and case analysis, scored by experienced clinical pharmacists.2 Even the top-performing model made high-risk errors: across models, 75% of responses omitted a critical contraindication in a test case, and no model correctly identified a specific prescription-duration limit issue embedded in a test scenario.2 The authors’ conclusion is directly applicable to practice: current-generation generative AI shows real promise as a pharmacist assistance tool, but performance gaps in complex reasoning and contraindication checking currently preclude autonomous clinical decision-making — human-AI co-review remains necessary.2
Bottom Line for Pharmacists
AI tools are most reliable for pattern-recognition tasks over structured data — interaction screening, dosing optimization, ADE prediction — where the underlying evidence base is well established. Generative AI/LLM tools are useful as a second check or drafting aid, not a substitute for pharmacist verification, particularly around contraindications and edge-case scenarios. Treat any AI-generated clinical recommendation the way you would treat a trainee’s: worth having, but requiring your sign-off.
References
- Nelson SD, Walsh CG, Olsen CA, et al. Demystifying artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020;77(19):1556-1570. https://doi.org/10.1093/ajhp/zxaa218
- Li L, Du P, Huang X, Zhao H, Ni M, Yan M, Wang A. Comparative analysis of generative artificial intelligence systems in solving clinical pharmacy problems: mixed methods study. JMIR Med Inform. 2025;13:e76128. https://doi.org/10.2196/76128
- Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar TM. Artificial intelligence in the field of pharmacy practice: a literature review. Explor Res Clin Soc Pharm. 2023;12:100346. https://doi.org/10.1016/j.rcsop.2023.100346