How Artificial Intelligence is Transforming Pharmacy Practice—and What It Means for Patient Safety

Artificial intelligence (AI)—the use of technology to perform functions that normally require human intelligence—is rapidly finding its footing in pharmacy practice.¹ From predicting adverse drug reactions to automating dispensing, AI is already reshaping how pharmacists work, and its potential to improve patient care is significant.²

Predicting and Detecting Adverse Drug Reactions

One promising area is the use of AI to predict and detect adverse drug reactions (ADRs). In one study, a machine learning (ML) technique called decision tree induction was applied to analyze the structural, chemical, and physical properties of compounds likely to cause ADRs, yielding high predictive accuracy for renal, central nervous system, and hepatic reactions.² In another approach, Bean et al developed a knowledge graph linking 4 node types—drugs, protein targets, indications, and adverse reactions—to classify known causes of adverse events.² Together, these models give pharmacists a clearer picture of a drug's risk profile, enabling more informed clinical decisions and better patient outcomes.²

Identifying Drug-Drug Interactions

AI is also being used to flag dangerous drug-drug interactions (DDIs). Song et al built a DDI predictor that draws on 5 types of drug similarity data: 2D structural molecules, 3D pharmacophores, drug interaction profiles, drug targets, and adverse effects.² Separately, Van Laurie et al developed an algorithm that predicts QTc prolongation and issues alerts when DDIs increase that risk.² By catching these interactions early on, AI tools can reduce ADRs and, in turn, lower health care costs.

Clinical Decision Support

Clinical decision support systems (CDSSs) bring together targeted clinical knowledge, patient information, and health data to guide medical decisions.² In practice, a CDSS matches an individual patient’s information to a clinical knowledge base and surfaces patient-specific recommendations for the pharmacist to review. This helps reduce medication errors and supports better treatment decisions.²

Automation in Community Pharmacy

In the community pharmacy setting, robotic dispensing systems are becoming more common. These systems typically include 3 components: an automated dispensing robot operated by pharmacy support staff, a dedicated robot for powdered medications, and a bar-coded medication dispensing system with digital guidance.²

Chatbots are another emerging tool in community pharmacy. Acting as virtual customer service representatives, they handle routine inquiries and basic complaints, routing more complex issues to staff. The result is a more efficient workflow, freeing pharmacists and technicians to focus on higher-priority tasks.²

Personalized Dosing

AI and ML are also being applied to dosage optimization. Algorithms that incorporate safety metrics, patient response, treatment history, disease knowledge, and electronic health records can predict and adjust effective dosages for individual patients.² One notable example is a dosing optimization system for chronic disease management that improves chemotherapy precision by tracking treatment response over time and predicting dosage requirements within safe and effective ranges.²

The Bottom Line

AI is not here to replace pharmacists—it is here to make them better. These tools serve as guides and aides in delivering personalized care, but pharmacists retain the final say in all clinical decision-making. As AI becomes more embedded in daily practice, familiarity with these tools will be a genuine professional advantage, helping pharmacists provide safer, more effective care to every patient they serve.

REFERENCES

1. How AI is being used in health care—and what it means for you. Cleveland Clinic. December 22, 2025. Accessed April 6, 2026. https://health.clevelandclinic.org/ai-in-healthcare

2. Chalasani SH, Syed J, Ramesh M, Patil V, Kumar TMP. Artificial intelligence in the field of pharmacy practice: a literature review. Explor Res Clin Soc Pharm. 2023;12:100346. doi:10.1016/j.rcsop.2023.100346