AI Ethics and Innovation in Community Pharmacy Practice

Artificial intelligence (AI) is reshaping community pharmacy, and in this conversation, host Erin Albert, PharmD, JD, DASPL, chief of pharmacy relations, network, and professional affairs at Mark Cuban Cost Plus Drugs, interviews Timothy Aungst, PharmD, founder of the Digital Apothecary and professor of pharmacy practice at the Massachusetts College of Pharmacy and Health Sciences, about what that transformation means for practice, ethics, education, and the future workforce.

Aungst defines AI in health care broadly as any technology that mimics human actions, behaviors, or thought processes, from simple chatbots to sophisticated AI agents. Although public attention focuses on large language models like ChatGPT and Claude, he stresses that AI in pharmacy has deeper roots, including early “expert systems,” business intelligence tools, and longstanding use of computer vision for pill verification and dispensing automation.

Albert and Aungst explore how AI is already embedded in pharmacy workflows, such as IVR systems for patient intake, automation of prior authorizations, refill processing, and image-based verification systems used in the US and abroad. Some domains are mature, while others remain early-stage but rapidly evolving.

A major theme is the ethical and regulatory landscape. Aungst highlights the need for practical guardrails to build trust, prevent misuse, and avoid high‑profile failures—such as unsafe clinical chatbots—that could trigger backlash and slow innovation. He notes current regulation is fragmented state by state, making it difficult for developers and health systems to navigate.

From the clinician’s perspective, Aungst warns about automation bias, overreliance on AI, and the risk of “deskilling” or “misskilling” when AI reinforces bad patterns, especially in nuanced clinical areas. He argues pharmacy education must teach how AI works, how to question it, and where its data and limitations lie.

Looking ahead, Aungst predicts community pharmacists will increasingly move into clinical, patient-facing roles, using AI to offload distribution tasks while enhancing access, documentation, and patient communication.

“AI is a term that gets thrown around so much, even for me. It's a little frustrating, because I think most people, when they talk about AI these days, are really focused on things like large language models,” Aungst said. “These tools became massively available to the wider public in the past 5 years, and we've always had these terms of AI.”

You Can Find Aungst’s Paper Here