Alexander Gorny's Blog Post: AI Assistants in Pharmacy — Generics, Originals, and What Comes Next
When a pharmacist hands you a package, do you know whether it contains an original drug or a generic? Most people don't. But that choice affects price, availability, and sometimes patient trust. A new blog post by Alexander Gorny on vc.ru looks at how AI assistants are entering pharmacy counters to help with exactly this decision.
The article is part of a wave of practical experiments with artificial intelligence in healthcare. While much of the public conversation around AI in medicine focuses on diagnostics or drug discovery, the pharmacy counter is where patients meet the system. And it is here that the difference between generics and originals becomes a daily, tangible challenge.
This guide unpacks what Gorny's blog post covers, why the generics-vs-originals question is so important, and how pharmacies can approach AI assistants without falling into common traps. It also includes a practical implementation outline for teams that want to move in this direction.
What the Blog Post Says About AI in Pharmacies
Gorny's material describes how AI assistants are being integrated into pharmacy workflows to support pharmacists in drug selection. The focus is on the comparison of generics and original drugs — two categories that often have the same active substance but differ in price, excipients, and clinical evidence.
According to the blog post, project teams have already implemented AI-based tools that help pharmacists quickly assess substitution options. Instead of manually opening multiple directories, a pharmacist can now type a query or scan a prescription, and the assistant returns structured information: international nonproprietary name (INN), dosage, bioequivalence data, local availability, and price differences.
The authors of the post emphasize that the assistant is not meant to replace the pharmacist. It is a decision-support layer that reduces routine work, frees time for patient consultation, and flags potential issues that a busy human might miss.
Generics vs. Originals: A Quick Refresher
To understand the significance, it helps to recall the basics. An original drug is the first product developed and tested for a new molecule. It goes through years of clinical trials and arrives with a patent that prevents copying for a limited period. After the patent expires, other manufacturers can produce generics — products with the same active substance, same dosage form, and equivalent clinical effect.
The key regulatory concept is bioequivalence. A generic must show that it releases the active ingredient into the bloodstream at a similar rate and to a similar extent as the original. Regulators
review these products through strict assessment procedures before granting approval. Typically, a generic must demonstrate that its 90% confidence interval for the area under the curve (AUC) and peak concentration (Cmax) falls within 80–125% of the original drug's values. This is not a guarantee of identical performance in every patient, but it is a scientifically robust standard that makes interchangeability defensible in most clinical scenarios.
However, the equivalence does not extend to every attribute. Excipients — the inactive ingredients that shape how a tablet dissolves, how it tastes, or whether it contains lactose or certain dyes — can differ significantly. For most patients, these differences are irrelevant. For a small subset, they can trigger allergies or affect absorption. This is where the judgment of a pharmacist becomes indispensable, and where an AI assistant that only compares active substances can lead to incomplete advice.
That is precisely why Gorny's post stresses pairing data with context. The AI does not just list the generic alternatives; it also surfaces the original's excipient profile, highlights known intolerance patterns, and notes any documented cases where switching caused clinical issues. The assistant's value lies not in saying
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