By Grace Mahas and
Jonathon Brown
Artificial intelligence has quietly entered the aisles of retail pharmacies, reshaping how medications are dispensed, managed and monitored. Picture a busy community pharmacy where an AI system double-checks prescriptions for errors in seconds, flagging a dangerous drug interaction that a fatigued human might miss. Now picture a patient with chronic pain, denied their usual medication because an opaque algorithm decided their risk was too high. These contrasting scenarios highlight the dual nature of AI in pharmacy: a powerful tool for efficiency and safety, and a potential “invisible gatekeeper” influencing who gets what treatment. AI is quickly transforming health care — and retail pharmacies are no exception. From automated prescription workflows to AI-powered risk screening, technology is reshaping how pharmacists support patients. But with innovation comes new questions: Is AI becoming the pharmacist’s greatest tool or an unintended barrier to care? The reality may be a bit of both.
AI in the aisles: case studies and real-world examples
• Prescription verification and workflow automation — Pharmacies dispense millions of prescriptions annually, and even a tiny error rate can harm patients. AI is being leveraged to act as a tireless second verifier. For example, AI-driven computer vision systems can identify pills by shape, color or imprint and match them to the prescription, helping catch dispensing errors. In one human-centered design study, pharmacists tested an AI that scanned filled prescriptions and only alerted them when it detected a discrepancy in the medication or dose. They likened this AI to a “vigilant guardian” working in the background — not a supervisor, but a teammate that flags potential mistakes. Pharmacists in the study favored a hybrid workflow where routine verifications could be automated, but any high-risk or uncertain cases would trigger human review. In practice, such AI-powered verification can reduce the cognitive load on pharmacists and streamline workflow. Large pharmacy chains and mail order pharmacies have also introduced robotic dispensing systems that count, fill and label medications. These robots, guided by AI and integrated with pharmacy software, can handle a significant portion of daily prescriptions with precision. A typical system might store the top 100 most commonly used tablets and capsules and automatically fill 50% to 60% of orders, freeing staff for other tasks.
• Medication eligibility assessments (prior authorization) — Not all prescription delays come from slow pharmacists or insurers — sometimes an AI is behind the curtain. When you drop off a prescription for an expensive medication, an algorithm might immediately scan your medical history and insurance policy to decide if you “qualify.” Health insurers are increasingly using AI-driven systems to automate prior authorizations and coverage decisions. In theory, this speeds up approval for medications that meet guidelines. In practice, there have been alarm bells. Physicians report that some AI tools are denying coverage at astonishing rates — one survey found automated systems produced denials up to 16 times higher than normal for certain treatments. These AI algorithms can systematically reject claims or require additional steps without any human intervention, ostensibly saving costs, but at the expense of patients waiting for care.
• Risk Detection — Retail pharmacies aren’t just dispensing drugs — they increasingly serve as touchpoints for public health monitoring. AI tools are now analyzing prescription data to detect patterns of potential abuse or health risk. A prominent example is the system called NarxCare, used in over 40 U.S. states and by major pharmacy chains to combat the opioid crisis. NarxCare crunches data from prescription drug monitoring programs (PDMPs) — databases of controlled substance prescriptions — and generates “Narx Scores” for each patient to indicate their risk of misusing narcotics, sedatives or stimulants. An AI-driven Overdose Risk Score factors in how many doctors and pharmacies a patient visits and the dosages they receive, aiming to alert pharmacists and doctors to patients who might be at high risk. In theory, this helps health professionals intervene early to prevent addiction or overdose. Indeed, one study suggested the Narx score could be a useful universal opioid risk screener.
But what happens when the computer gets it wrong? Unfortunately, some patients with chronic pain have found themselves flagged as risks and abruptly cut off from medication — not by a conscious decision of their pharmacist, but by the silent verdict of an algorithm. Patient advocates report “a lot of patients [are] cut off without medication,” with some so desperate they consider street drugs or even suicide when their pain treatment is blocked. Such outcomes underscore the fine line between risk management and gatekeeping.
• Personalized medication recommendations — One of the most exciting promises of AI in pharmacy is moving from one-size-fits-all dispensing to truly personalized medicine. Pharmacists have always tailored advice to patient needs, but now AI gives them supercharged analytical abilities. By quickly sifting through a patient’s medical records, genetic information and even wearable device data, AI can suggest optimized medication regimens that a human might not immediately consider. For instance, AI algorithms can propose the ideal blood pressure medication from dozens of options by analyzing which choice would best complement the patient’s other conditions and genetic profile — all in a fraction of the time it would take to manually research. In practice, we are seeing decision-support tools that help pharmacists recommend dose adjustments or alternative therapies based on patterns in patient lab results or previous responses. One recent exploration demonstrated how a large language model (akin to ChatGPT) could review a complex patient case and provide medication adjustment suggestions for a pharmacist to consider. AI can also crunch vast clinical datasets to identify drug interactions that are so rare or nuanced that they aren’t obvious, then alert the pharmacist with a personalized warning for the patient in front of them.
Considerations
The integration of AI into pharmacy care brings not only innovation but also a thicket of regulatory and ethical questions. One major concern is the transparency and explainability of AI decision making. Unlike a human pharmacist, a machine learning algorithm can be a “black box,” making recommendations or decisions without an obvious rationale. This opaqueness is problematic in health care, where knowing “why” is crucial for trust. The issue came to a head with tools like NarxCare: The company behind it did not disclose exactly how its overdose risk algorithm worked, even as its scores were used to influence patient care. Health authorities noticed. The Centers for Disease Control and Prevention (CDC) cautioned clinicians about over-reliance on such scores, warning that many risk scores are generated by proprietary algorithms that are not publicly available and thus could produce biased or misleading results. Essentially, if an AI flags a patient as high-risk, the patient and their doctor deserve to know the basis for that flag — was it the number of prescriptions? The combination of meds? — to ensure it’s fair and accurate. Lack of transparency can lead to what feels like arbitrary “invisible gatekeeping,” where patients are denied medication by an algorithm with no explanation. This runs directly against the grain of medical ethics, which prioritize informed decision making and consent.
Hand in hand with transparency is the issue of bias in AI-driven assessments. AI systems are only as good as the data they learn from, and if that data reflects societal biases or gaps, the AI can end up amplifying those problems. In the pharmacy context, a risk-scoring AI might unintentionally give higher risk scores to certain groups of patients because of factors correlated with demographics rather than true risk. For example, if historically patients in rural areas saw multiple doctors (due to access issues) and that feeds into an opioid risk algorithm, the AI might label all rural patients as riskier, which is a form of geographic bias. In one well-known case outside pharmacy, a hospital algorithm was found to systematically underestimate the illness severity of Black patients because it used health care spending as a proxy for health — and historically less was spent on Black patients, skewing the results. Similar pitfalls could occur in pharmacy AIs. If an AI recommending treatments has mostly clinical trial data from younger adult patients, it might under-recommend effective therapies for the elderly or for women, simply because it “learned” from a biased sample. Regulators are increasingly aware of these risks.
AI in retail pharmacy is both a transformative force and a potential gatekeeper. It holds the promise of fewer errors, greater efficiency and personalized care on a scale previously unimaginable in a corner drug store. Yet it also poses new questions about fair access, accountability and the preservation of the human touch in healing. The coming years will likely see pharmacies refining this balance: embracing the innovation while implementing safeguards against the gatekeeping effect. If done thoughtfully, the pharmacy of the future will be one where AI and pharmacists work in tandem — the speed and data crunching of a machine with the care and compassion of a human — ultimately delivering superior health care service to patients. That is a future worth striving for, as long as we keep a watchful eye on the gate.
Grace Mahas and Jonathon Brown are director of content and chief growth officer, respectively, at Trend Hunter.
