# AI-Generated Content in Pharmaceuticals: A Cautionary Tale

> An early generative-AI experiment in pharma showed impressive fluency alongside a serious risk of fabricated evidence and references.

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Date: 5 April 2023

An early experiment with generative AI highlighted both the impressive fluency of AI-generated pharmaceutical content and the serious risk of fabricated evidence and references.

Generative AI can produce polished, authoritative-looking text in seconds. For pharmaceutical teams, that immediately raises attractive possibilities: faster first drafts, summaries, research support and more efficient creation of internal materials.

Our early testing also highlighted a fundamental problem. Fluent language is not the same thing as reliable evidence.

## Convincing content can still be wrong

When asked to generate healthcare and pharmaceutical content, the AI was able to create text that sounded coherent and plausible. The structure was good, the tone was confident and the arguments often appeared credible at first glance.

However, closer review exposed factual weaknesses. Most importantly, the system could provide references and sources that looked legitimate but were not real. This is a particularly serious failure in pharmaceuticals, where apparently minor inaccuracies can contaminate subsequent analysis or create compliance and reputational risk.

## The source problem

Generative models are designed to produce likely language, not to guarantee that every factual statement is backed by a verified source. A fabricated citation can therefore be presented with exactly the same confidence as a genuine one.

That creates an asymmetric risk: AI can make drafting dramatically faster, but the apparent professionalism of the output may increase the amount of expert verification required. If users assume that well-written text has already been fact-checked, errors can travel quickly into presentations, models or decision documents.

## What this means for pharmaceutical use

AI-generated material should be treated as a draft or analytical aid rather than a source of truth. Claims should be checked against primary evidence, references should be opened and verified, and high-impact outputs should remain subject to appropriate expert review.

The technology is still extremely promising. Used carefully, it can accelerate ideation, summarisation and repetitive content tasks. But in regulated or evidence-heavy environments, speed is valuable only when paired with controls that preserve accuracy and traceability.

## The principle still holds

The most useful lesson from these early experiments is not that AI should be avoided. It is that organisations need to understand what the technology is actually good at. Generating a plausible first draft is very different from generating validated evidence.

Human expertise therefore remains essential — not necessarily to write every sentence, but to determine what can be trusted, what must be checked and where AI-generated material is appropriate in the workflow.

*This article reflects the market and policy context at the date of publication.*

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