AI on the Clinical Front Line: The New Visibility Challenge for Biotechs

By Andrew Nelson, Senior Director, Integrated Intelligence + Analytics Social Listening

In the AI era, time pressed clinicians are already changing how they keep up with vast quantities of medical information now at their fingertips. And that shift has direct implications for biotech visibility. Our March 2026 analysis by the Real Chemistry Integrated Intelligence team shows that generative AI use is now mainstream: over half of healthcare professionals already use it in a professional capacity, 65 percent use it multiple times a week in clinical settings, and almost all—97 percent—expect their use to increase.

When physicians increasingly start with AI to make sense of vast volumes of research on the clinical frontline, the first answer they see may not come directly from a biotech company, its congress booth, published data, or website. So what does that mean for biotechs?

In a fast‑paced, high‑volume information environment, visibility is no longer a future issue – it’s a right‑now challenge. The urgent question biotechs must navigate is how to ensure their science is visible, credible, and accurately represented in the answers large language models (LLMs) deliver first.

The new challenge and opportunity

This shift comes with both risks and opportunities for biotechs, which often have strong science but a smaller digital footprint than big pharma. In an AI-driven information landscape, that can translate into fewer accessible, structured, citable sources and even third-party mentions for AI tools to retrieve and draw on.

Given that 64 percent of clinicians tell us they rely on AI to keep up with medical research, the question is no longer only “Do we have good data?” It also needs to be:

“Can AI find, interpret, and explain our science accurately?”

So, although AI isn’t replacing peer-reviewed evidence, guidelines, or scientific exchange, it is increasingly shaping the path to how they’re reached—which is a strategically important shift for biotech teams with lean budgets and high-value milestones.

Unlike larger companies, biotech often can’t afford to be everywhere at once. But this is also where the rise of AI search creates a new efficiency opportunity: if a company’s most important scientific content is clear, structured, credible and easy for AI to retrieve, that content can work harder across multiple information journeys.

In other words, AI doesn’t eliminate the need for multichannel engagement, but it increases the return on getting the fundamentals right.

So what does “getting it right” actually mean?

This isn’t about chasing algorithms or producing more content for its own sake. Visibility in the AI era is no longer just about being present. It’s about being more intentional about findability at precisely the moment first impressions form. And, perhaps most importantly: knowing how your company, your science, or your disease area is currently being represented in AI-generated answers.

For biotechs, the implications are particularly significant. Early perception is now often shaped by a limited number of outputs: a congress presentation, a publication, a data release. If those moments are not translated into content that AI can find, cite, and explain, important scientific value may simply miss the clinicians, investors, and partners biotechs are trying to engage.

And what shows up frames how options are discussed, how risk is understood, and how innovation is perceived in real clinical and commercial conversations.

Smarter choices for maximum impact

The good news is this is a challenge biotechs can address—by seeing how their science is being picked up today and acting on it. Companies can assess whether their content is appearing in AI responses, which sources are being cited, how their science is being framed, where competitors are more visible, and what content gaps may be limiting discoverability.

That kind of analysis does more than diagnose visibility. It helps teams make smarter choices about where to invest limited communications effort for the greatest impact. And it is also a valuable measurement tool. Medical teams are increasingly asked for real-world evidence showing the impact of their work. With LLMs now a new and established stakeholder, being able to show visibility and key message pull through on these platforms should be a key performance indicator as it accurately demonstrates value.

At Real Chemistry, we see this as part of a broader shift in medical communications and integrated intelligence: helping organisations understand not just what they are saying, how, and who to—but how their science is being found, interpreted and reused in an increasingly AI-influenced world. Not just for doctors, but patients too. Our research shows 64% of consumers trust generative AI for health information; 60% are moving away from traditional search, and nearly half (49 percent) already see AI as equally reliable as their healthcare provider. And let’s not forget that people have been turning to Dr Google and social media for medical information and advice for a number of years now.

Getting your science seen

That makes this a timely challenge for biotech—and one we’ll be exploring at UK Biotech Week. In our session, we’ll look at what rising AI use among doctors and patients means for biotech companies trying to build scientific credibility, improve discoverability, and communicate more effectively in a rapidly changing information environment.

In the age of AI, strong science still needs to be found. So, if biotech teams want their science to shape the conversation, they need to know whether AI can see it, trust it, and use it. And those that don’t pay attention to how their information shows up risk more than being overlooked. They risk quietly not being part of the conversation at all.

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