Seeing your science through AI’s eyes

AI is rapidly transforming how healthcare professionals discover, interpret, and trust scientific evidence. Tools such as ChatGPT, OpenEvidence, and Gemini are no longer experimental – they are redefining how information is surfaced, synthesized, and used in real-world decision-making.

As these systems evolve, one critical question is emerging: How visible is your evidence when AI looks for answers?

Why AI visibility matters

AI does not search the way humans do. Instead of listing sources, it integrates and synthesizes information based on accessibility, structure, and authority.

That means even high-quality, peer-reviewed data may be harder for AI to find – or appear lower in generated outputs – if it is not structured or tagged in ways that models can interpret.

Factors such as open-access availability, metadata quality, schema structure, publication timing, and source credibility all influence how AI tools prioritize and represent content.

“For Medical Affairs and Scientific Communications teams, this represents a new challenge: ensuring that credible evidence is not only accurate and compliant, but also discoverable within AI-driven ecosystems,” says David Segarnick, PhD, Chief Medical Officer & Executive Vice President, MEDiSTRAVA, Inizio Medical.

“This is particularly important for agents with novel mechanisms of action in treatment-resistant and rare diseases – where the visibility of validated evidence can shape clinical understanding and uptake.”

Introducing Generative Engine Optimization (GEO)

As generative AI becomes a new lens for evidence discovery, a new strategic capability is emerging: Generative Engine Optimization (GEO).

At Inizio Medical, we are exploring how GEO principles can help identify the factors that influence how AI platforms interpret, prioritize, and present scientific information.

By understanding the biases that affect AI-driven visibility – such as recency, indexability, accessibility, and authority – Medical Affairs and Scientific Communications teams can begin to refine content strategies, metadata practices, and dissemination frameworks that ensure credible science remains both visible and trusted.

This is not about gaming algorithms. It is about understanding how AI perceives and values information, and using those insights to ensure that validated, peer-reviewed evidence is represented accurately in the AI era.

From discovery to dissemination

AI is reshaping how scientific evidence is surfaced, shared, and trusted.

Those who adapt early will help define the new standards for how credible science is recognized within AI-mediated environments.

Explore how we can help your evidence stay visible and trusted in the age of generative AI.