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Medical Affairs Builds the Science. AI Decides Who Finds It.
The content your team produces is accurate, peer-reviewed, and compliant. The question is whether it exists anywhere a physician, a payer, a patient, or an AI system can actually find it.
By Divyesh Khetia
Idea in Brief
Four distinct audiences now make clinical and access decisions using AI-generated content. Medical Affairs content reaches none of them effectively in its current form.
This is not a content quality problem. The science is excellent. It is an architecture problem.
The compliance process Medical Affairs has always operated within is not a barrier to AI visibility. It is a competitive moat. The organizations that build the distribution architecture to use it in 2026 will define the standard for the next decade.
The first-mover window is open. It will not stay open.
The Shift
The Search Paradigm Has Changed
For two decades, pharmaceutical content strategy operated on a simple premise: produce scientifically rigorous material, distribute through approved channels, and trust that physicians and patients would find it through search. That premise is no longer reliable.
In February 2024, Gartner forecast that traditional search engine volume would decline 25% by 2026 as generative AI tools became substitute answer engines. We are living that transition now.
The scale is documented. OpenAI reported in January 2026 that more than 230 million people globally ask health and wellness questions on ChatGPT every week. This figure is based on the company's own de-identified analysis of user conversations. Health is one of the most common uses of the platform, with approximately 40 million daily health queries.
Among physicians specifically, the American Medical Association published survey data in February 2025 showing that 66% of doctors reported using health care AI in 2024, a 78% increase from the 38% who reported AI use in 2023. The tools in use extend well beyond administrative tasks into active clinical information-seeking.
Medical Affairs teams optimizing content only for traditional search are building infrastructure for a channel that is contracting. Three audiences have already migrated. The payer is the fourth, and it is following.
The Framework
Four Audiences. Four Channels. One Unmet Need.
The structural failure in most Medical Affairs content strategies is not a quality problem. It is an architecture problem. The content exists. The audiences are not finding it. Understanding why requires mapping the four distinct channels through which clinical and access decisions are now being shaped by AI.
Audience 01
The Physician at the Point of Care
Clinical AI platforms have achieved physician adoption at a speed and scale that few technology categories in medicine have ever reached. As of January 2026, the leading medical AI platform in the United States serves over 40% of U.S. physicians daily across more than 10,000 hospitals, facilitating over 20 million clinical consultations per month, up from approximately 3 million per month just one year earlier.
These platforms retrieve answers from structured, peer-reviewed evidence bases. Medical Affairs content that is not published in indexed journals, not referenced in clinical guidelines, and not structured for natural-language clinical queries does not reach this audience. It does not matter how accurate it is. It is simply not in the retrieval pool.
Source: Clinical AI platform Series D press release, January 2026; Series B press release, July 2025.
Audience 02
The HCP Using General AI Search
Not all physician information-seeking happens at the point of care. A 2025 survey of 126 verified HCPs found that over half currently use AI in some capacity for professional purposes. Separately, a search behavior analysis found that approximately 84% of healthcare-related searches now trigger an AI overview. AI adoption among physicians nearly doubled in a single year according to AMA data published in February 2025.
This audience is searching with natural-language, question-form queries: mechanisms, clinical evidence, treatment sequencing, and patient management. Medical Affairs content built as dense scientific literature is not optimized for retrieval in this context. It requires restructuring: layered explanation, FAQ architecture, and broad distribution across authoritative platforms.
Source: Varn Health HCP survey, n=126, 2025. AMA Physician AI Sentiment Report, February 2025.
Audience 03
The Payer and Health Technology Assessor
Formulary decisions and health technology assessments increasingly involve evidence synthesis teams using AI to process and evaluate clinical data rapidly. This audience is not browsing. It is conducting structured evidence reviews under time and resource pressure, often across multiple therapeutic areas simultaneously.
The consequence of invisibility here is not a missed engagement. It is a missed access decision. If the evidence base supporting a therapy does not surface clearly and credibly in the AI-assisted review process a payer team is running, it affects formulary positioning, reimbursement criteria, and ultimately patient access at scale. This audience requires content that answers comparative effectiveness questions, health economic outcome questions, and evidence quality questions. All of these must be answered explicitly and in retrievable form.
Audience 04
The Activated Self-Pay Patient
This is the newest audience and, in therapeutic areas with significant direct-pay activity, the most urgent. A growing segment of patients is arriving at clinical encounters having already formed a treatment view sourced from AI-generated content. A portion bypass the clinical encounter entirely.
The scale is significant. OpenAI data published in January 2026 shows approximately 70% of healthcare-related AI conversations occur outside normal clinical hours, precisely when patients are making independent decisions. Roughly 3 in 5 U.S. adults reported using AI for healthcare questions in the three months to December 2025. More than 40% said they used AI specifically to learn about treatment options.
The information environment these patients encounter is increasingly under regulatory scrutiny for accuracy. The FDA issued warning letters to 30 companies in March 2026 for false or misleading promotional claims. This was the second major enforcement wave since a September 2025 crackdown that produced more regulatory letters in six months than in the prior decade combined.
Medical Affairs has the scientific authority and the compliance infrastructure to produce something better. The strategic gap is distribution architecture.
Source: OpenAI usage report, January 2026; FDA press release, March 3, 2026; Holland & Knight, September 2025.
The Insight
The Compliance Advantage
Here is the counterintuitive insight most Medical Affairs teams miss. The characteristics that make compliant pharmaceutical content difficult to produce (balanced risk-benefit presentation, citation requirements, regulatory review, evidence grounding) are precisely the characteristics that AI systems are trained to value and retrieve.
LLMs are not agnostic about source quality. They favor content with clear structure, factual accuracy, cited evidence, and strong authority signals across trusted platforms. Medical Affairs content built to PAAB, OPDP, and MLR standards is built to the exact quality signals that AI retrieval systems prioritize.
The compliance process is not a barrier to AI visibility. It is a competitive moat. But only if the content is structured and distributed in a way that AI systems can retrieve it.
The Strategic Implication
Every organization in life sciences is building toward GEO. The ones that arrive first with compliant, structured, multi-audience content architecture will occupy the authority positions that are hardest to displace. The window is open. It will not remain so.
The Framework
Four Decisions Medical Affairs Must Make
Generative Engine Optimization is not a replacement for traditional content strategy. It is an architectural layer applied on top of existing scientific evidence. For Medical Affairs teams, it requires four deliberate decisions.
Query Architecture by Audience
The clinical AI audience searches in technical clinical language. The payer searches for comparative effectiveness and health economic outcomes. The HCP uses professionally framed conversational queries. The self-pay patient uses natural language. A single content architecture cannot serve all four. Each requires its own query mapping, content layer, and distribution logic.
Structure Over Density
The characteristic failure mode of Medical Affairs content for AI retrieval is density without structure. Long-form scientific documents are written for expert human readers. AI retrieval systems parse structure: headings, logical flow, FAQ sections, layered explanation beginning with accessible summary and adding clinical detail. Content must be restructured for AI comprehension without losing scientific precision.
Authority Distribution
For clinical AI platforms, content authority flows through peer-reviewed literature. The primary channel is not website optimization. It is ensuring the scientific evidence base is published in the journals and guideline sources clinical AI platforms license. For general AI search, authority requires cross-platform presence: peer-reviewed citations, medical society recognition, ungated content where compliant, and consistent structured data.
Patient Content as a Distinct Track
Patient-accessible scientific content is not a scaled-down version of HCP content. It is a separate category requiring its own development process, query mapping, compliance pathway, and distribution architecture. Patients will find and use health information from AI tools regardless. The choice Medical Affairs faces is whether the information they find is accurate, balanced, and evidence-based, or whether it comes from sources currently under active regulatory enforcement.
The Opportunity
The First-Mover Window
The pharmaceutical industry moves slowly on content architecture. The GEO frameworks that will define Medical Affairs content strategy for the next decade are being built right now. The first organizations to map queries across all four audiences, restructure content for AI retrieval, distribute through clinical platform partnerships, and develop compliant patient-facing content will occupy the authority positions that are difficult to displace once established.
The patient-facing and direct-pay channel represents a first-mover opportunity that is almost entirely uncontested. There is currently almost no compliant, evidence-based patient content in the AI search ecosystem for direct-pay therapeutic areas. The field is occupied largely by commercial content under active FDA enforcement. The organization that builds accurate, accessible, AI-optimized patient content now does not face competition from other pharmaceutical companies. It faces a vacuum.
The window is open. Content architecture decisions made in 2026 will determine visibility trajectories for years. The question is not whether to act on this. It is whether to act before or after the window closes.
FAQ
Practical Questions
What is GEO and how does it differ from SEO?
Traditional SEO optimizes content for search engine ranking algorithms that surface links. GEO optimizes content for retrieval by large language models that synthesize answers. The goal shifts from ranking to being cited. This requires structured content architecture, authority distribution across trusted platforms, and query mapping for the natural-language questions AI systems are actually being asked.
Does Medical Affairs have the authority to own this initiative?
Yes, and it is uniquely positioned to do so. Medical Affairs is the only function with simultaneous access to scientific evidence, regulatory compliance infrastructure, and medical credibility. Marketing cannot produce content with the scientific authority required for clinical AI platforms. Regulatory will not approve patient content that is not evidence-grounded. Medical Affairs sits at the intersection the GEO era demands.
How does the payer audience differ from clinical AI in content requirements?
Both require evidence-grounded, structured content. But the query types differ fundamentally. Clinical AI platforms handle point-of-care questions about mechanisms, dosing, and treatment decisions. Payer and HTA teams are asking comparative effectiveness, health economic outcome, and evidence quality questions. Content built only for clinical retrieval will not surface effectively in a formulary review context. These require parallel but distinct content tracks within the same evidence architecture.
How does compliant content become visible in clinical AI platforms?
Clinical AI platforms primarily ingest peer-reviewed medical journal content. The pathway to clinical AI platform visibility runs through the scientific publication strategy, not through website optimization. Content published in indexed journals, referenced in clinical guidelines, and distributed across the medical databases these platforms license will be retrieved. Content that lives only on company-owned websites will not.
What is the realistic timeline for Medical Affairs to achieve AI search visibility?
For clinical AI platforms, the timeline tracks the publication cycle: 12 to 24 months for systematic publication in high-impact journals to flow into retrieval systems. For general AI search, structured content with strong authority signals can achieve meaningful visibility in 3 to 6 months. For patient-facing content, there is no competitive ceiling currently. Organizations starting now are building into an empty field.
If this shifted how you think about Medical Affairs content strategy, share it with someone who should be having this conversation.
Level Up publishes every week for leaders in Medical Affairs, life sciences strategy, and artificial intelligence.
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Published every week by Div Khetia.
