Amanda SapioWhen Sherry Yun Wang, Ph.D., arrived at Chapman University’s School of Pharmacy in 2021, artificial intelligence was already transforming parts of healthcare, but it was not yet widely accessible in everyday clinical training and practice.
Wang saw where it was headed — and set out to bring it into Chapman’s classrooms and labs. Wang, an assistant professor of pharmacy, has become a leading voice in how the school approaches healthcare AI. In 2025, she was named Women in AI’s AI Researcher of the Year for North America and received a $250,000 grant from the National Institutes of Health to conduct a study on bias in AI prescription tools. That bias can show up when algorithms flag patients based on patterns that don’t tell the full story. For example, frequent pharmacy changes may reflect life circumstances, not misuse. Her research spans epidemiology (the science of public health), health analytics, pharmacoeconomics (the health economics of pharmaceutical products and services), and clinical pharmacy. It focuses on NarxCare, an AI platform that analyzes Prescription Drug Monitoring Program data.
As AI becomes more embedded in healthcare, Wang is bringing those questions into the classroom, helping students understand how these tools work in real clinical settings.
“Part of embracing this technology is recognizing that we can’t send graduates into the world without knowing how to use these tools,” said Jennifer Totonchy, Ph.D., associate dean of research and graduate programs. “Ensuring our students are comfortable with AI is part of how we prepare them for future careers and stay true to our mission.”
WHEN AI FALLS SHORT
Wang began studying the dataset and quickly saw troubling patterns. With a research background in opioid use, she focused on opioid prescriptions.
“I found that NarxCare is embedded in other states’ Prescription Drug Monitoring Programs,” Wang said. “The California Department of Justice never allowed it in California, and we are very lucky for that. In other states, some patients are denied refills — especially opioid prescriptions — because of NarxCare’s AI.”
NarxCare uses certain predictors to assess whether a patient may be misusing opioids, but those signals can be misleading. The system flags patients who use multiple pharmacies or physicians, or have overlapping prescriptions. In practice, those patterns don’t always reflect misuse. They can also describe patients who move frequently, lack transportation, or live in medically underserved areas.
“California’s rent is very high, and many people from lower socioeconomic backgrounds are forced to move to a different zip code every year or two,” Wang said. “As they move, their doctors and pharmacies change. NarxCare flags those patterns and labels them as misuse — and then patients are told their prescriptions can’t be refilled.”
Wang has heard the impact firsthand in conversations with patients at conferences.
“Some patients began crying when sharing their stories because they were in so much pain,” she said. “Pain is pain. This is not something people should be forced to live with.”
A FOCUS ON ETHICAL AI
To address bias in clinical decision-making tools, Wang founded the Healthcare AI Initiative (HAI) at Chapman, bringing together researchers working on pressing healthcare challenges. HAI examines ethical questions surrounding AI, including data privacy, and develops equitable machine learning approaches.
Wang also developed an AI elective within the School of Pharmacy, focused on practical applications for future clinicians.
Totonchy sees the skepticism around AI in higher education as an opportunity.
“Generative AI quickly gained a negative reputation because it was seen as a tool for cheating,” she said. “That misses the point. These tools can work alongside humans, handling routine tasks so people can focus on analysis and decision-making. The sooner institutions move past resistance, the sooner AI can support that work.”
STUDENTS PUT AI TO WORK
In Wang’s classroom, pharmacy students with no coding experience use AI tools to run geospatial analyses and answer research questions rooted in clinical practice. She walks them through the process in real time, showing them how to upload datasets and guide an AI tool to generate code.
“Every pharmacist has heard the rumors that AI will take pharmacists’ jobs,” Wang said. “But early exposure helps students. As future clinicians, they learn where the real focus should be in relation to AI. That’s something my students learn in this course.”
Wang also works with students on research projects, extending that learning beyond the classroom. Two PharmD students, Elena Wu ’27 and Harvey Duong ’27, have collaborated with her on studies using AI to analyze large datasets.
“In one project, we analyzed over 64,000 prescriptions,” Duong said. “That kind of work would be difficult to do manually. Using machine learning made it faster and easier for us to interpret the results and identify gaps in healthcare equity.”
“We are also already seeing AI positively impact the quality of patient care in our pharmaceutical rotations, so having AI integrated into our curriculum is eye opening,” Wu said. “I don’t know many other schools that offer this kind of opportunity.”
Their work has reached a national audience. In March 2026, their research on the safety of certain diabetes and weight-loss medications was selected as one of ten Featured Research Posters at the American Pharmacists Association Annual Meeting.
“What they’ve achieved at such an early stage in their careers is truly exceptional,” Wang said. “I’m incredibly proud of their growth.”
