Healthcare professionals (HCPs) are increasingly relying on artificial intelligence to work through the vast sea of medical information. This technology functions as a hidden intermediary in the information ecosystem, consuming and interpreting data to surface answers during routine searches. Oncologists are already adopting these tools, with a significant majority using generative AI to find background information and compare viewpoints. When an HCP queries this system, they receive a synthesised answer without consciously selecting a specific publisher or journal, effectively bypassing the traditional hierarchy of scientific sources.
Scientific engagement has always been an ecosystem, not a linear path. HCPs seek knowledge, but they also rely on key opinion leaders, societies, publishers, and peers to shape that understanding. AI now functions as a distinct layer within this structure, influencing what professionals discover and how they learn. This shift requires a change in perspective from asking how to reach an audience to understanding who and what is shaping their thinking. Organisations must identify where AI is likely to influence these behaviors to ensure the right evidence is available when needed.
Effective engagement now requires a dual-layered approach. First-party data reveals audience needs, but a second layer of insight is essential: an understanding of the information ecosystem. Tools like GEO audits and discoverability analysis can help identify the types of questions being asked and the sources cited. In a recent audit, a client found that 55% of earned citations came from only eight sources. This data highlights how a small number of sources can exert disproportionate influence on perception and understanding.
Designing for people and machines
Scientific content must increasingly serve two distinct audiences: the people consuming it and the systems retrieving it. AI adds a critical requirement for content structure: it must be modular and easily interpretable by machines. Content is no longer just for human readers; it must also be designed for discoverability. When priority questions are asked, the visibility and accuracy of evidence determine if it becomes part of the answer. Placing science where audiences engage and where answers are formed is the core challenge.
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Strategic medical communications must adapt to this reality by rethinking how content is structured and targeted. This involves balancing targeted HCP environments with authoritative sources on professional platforms. Direct, permissioned channels, such as WhatsApp Business, play a vital role by delivering timely updates to opted-in professionals. These channels reinforce priority evidence at the moment it is most relevant. By integrating ecosystem insights with audience behavior, organisations can ensure that their science travels effectively through the wider information environment.
Measuring the invisible
Traditional metrics often fail to capture the impact of AI-mediated discovery. GEO and AEO audits provide a clearer picture by revealing what answer engines cite and where evidence gaps exist. These insights allow planners to prioritise questions and content placement while maintaining credibility. The goal is to maximise “Share of Answer”—ensuring that when a priority scientific question is asked, the correct evidence is part of the response.
As AI continues to shape the flow of knowledge, this approach ensures that science remains accurate and accessible. AI-mediated discovery often escapes traditional metrics.
