Health AI Assistants: the new front door to healthcare

A patient wakes up with recurring back pain and asks an AI assistant what to do. The app checks its history, notes a previous prescription, and suggests switching to a different anti-inflammatory. Your molecule isn’t mentioned. By the time the patient sees their doctor that afternoon for a prescription, the framing is set.

The adoption of GenAI assistants is already here, on both sides of the consultation. 200 million of ChatGPT’s 800+ million users submit health-related queries every week. Physicians are no different: 76% use AI in clinical decision-making, over 60% check drug interactions through it. Open Evidence, a clinical AI built for doctors, adds 65,000 new users every month.

Furthermore, the offer of GenAI assistants is expanding at a very rapid pace. In just one quarter, four of the biggest tech companies launched their own AI health assistants: ChatGPT health for OpenAI, MedgeMMA 1.5 and MedASR for Google, Amazon One Medical’s Health AI, and others.

For pharma, as AI search is projected to surpass traditional search by 2028, this is a burning platform on three fronts:

  • First, brand invisibility: every drug has two names, a brand name and a scientific name (the INN). When a brand’s content isn’t well-indexed in the sources AI models draw from, the answer defaults to the INN. Ask most AIs about allergy relief and you’re more likely to get “cetirizine 10mg” than a brand name, potentially bypassing years of advertising investment in a single response.
  • Second, loss of narrative control: these models don’t limit themselves to approved clinical evidence. They pull from Reddit, patient forums, blog posts, and synthesize it all into a single authoritative-sounding answer. As medical disclaimers in AI outputs have dropped from 26.3% to under 1% between 2022 and 2025, a user posting “this drug cleared my skin in two weeks” becomes, after aggregation, something the AI presents as near-medical evidence.
  • Third, off-label visibility: drugs are approved for specific use, diseases, populations and treatment lines. Anything else is “off-label”, and AI models don’t make that distinction. A cancer drug approved for second-line but studied for first-line? An AI may recommend it for first-line.

Together, loss of narrative control and off-label visibility raise a regulatory question pharma cannot ignore. FDA and EMA already require manufacturers to monitor adverse drug information across their digital channels, an obligation that has progressively expanded to social media. No regulator has yet ruled that AI outputs constitute a monitored channel. But the logical extension is hard to dismiss.

GEO and RAG: where your product’s AI visibility is actually decided

In traditional search, you compete for a position on a page of ten results. That was SEO. Today, two new battlefields are replacing it, at a fast pace, and they work differently.

The first is GEO (Generative Engine Optimization). This is how you appear in generalist LLMs (ChatGPT, Gemini, Perplexity) and the new health assistants. These systems pull from the open web and synthesize content into a single answer. You don’t compete for a ranking. You compete to be one of the two or three sources the model cites. What matters: web-indexed, machine-readable content such as patient education pages, Q&As, plain-language summaries, structured abstracts with consistent INN-brand pairing.

The second is RAG (Retrieval-Augmented Generation). This is how you appear in specialized clinical tools like Open Evidence or ClinicalKey AI, used by physicians at the point of care. These systems don’t search the web. They pull directly from curated medical databases such as PubMed, Cochrane, clinical guidelines. What matters here: publication quality, complete PubMed metadata, structured abstracts.

Two questions we get asked often: does AI know if a doctor is influential? And can it tell if a study is good? Short answer to both: sort of, but not the way a human would.
AI doesn’t look up a doctor’s credentials or read a study’s methodology. It takes shortcuts. If a doctor’s name appears frequently in published medical literature, AI will cite them more, not because it checked who they are, but because it keeps seeing their name. Same for studies: AI doesn’t assess sample size or statistical rigor. It looks at how often the study is referenced elsewhere, how well-known the journal is, and how easy the content is to extract. A groundbreaking trial buried in a lesser-known journal with incomplete metadata may never surface, while a smaller study in a top journal with a well-structured abstract gets cited everywhere. For pharma, this means AI visibility depends as much on where you publish and who signs the paper as on the quality of the science itself.

What changes for patients, GPs and specialists

The patient has moved from clicking through ranked links to acting on a single AI-synthesized answer. In France, 60% of those who received an AI health recommendation acted on it, 17% without seeing a doctor. Today, patients already arrive at consultations pre-informed, having queried generalist LLMs. For over the counter (OTC) products, the commercial risk is immediate and direct: if your brand name is not cited, it does not exist at the point of purchase decision. For prescription products, the risk is structural: AI is reshaping patient expectations upstream of the consultation.

The General Practitioner (GP) faces patients with AI-formed expectations and a shrinking role in routine care. The Utah precedent accelerated the structural shift further as it became the first US state to authorize AI-driven prescription renewals for 190 chronic-condition medications at $4 per renewal. As prescription renewals represent roughly 80% of all medication activity and routine prescriptions are being automated, the GP becomes an exception handler, and pharma’s most established touchpoint contracts.

The specialist is increasingly relying on clinical AI tools as a first-line filter before prescribing. What it surfaces shapes what gets considered. What it doesn’t surface doesn’t exist. This makes publication indexing, such as complete PubMed metadata, structured abstracts, top-tier journal placement, the single most important lever of visibility where clinical decisions are made.

What to do

Diagnose now. The starting point is understanding where you stand today.

Querying ChatGPT, Perplexity, Gemini, Open Evidence, and as they become accessible, health assistants, with the top ten questions your patients and doctors ask reveals the gap faster than any internal audit. For instance, “what’s the best treatment for type 2 diabetes with an HbA1c above 8?”: Does the answer mention your brand or just the INN? Does it cite your pivotal trial or a competitor’s? Does it state the right dosage? A product well-cited in generic ChatGPT may be entirely absent from a health assistant’s personalized recommendations if structured clinical data is incomplete. The resulting gap analysis is your exposure map.
Platforms like Profound or Evertune can help systematize tracking across platforms. This type of baseline audit produces actionable signal at near-zero cost.

Act on your existing corpus within 6 months. In most cases, the issue isn’t a lack of content, it’s a formatting problem. Reformatting existing assets can improve AI citation rates by up to 40%.

That said, not everything should be optimized: approved labeling and published trials are generally safe to reformat, while off-label research may carry compliance risk if made more visible without guardrails. It makes sense for Medical Affairs and Regulatory to scope that boundary early.

From there, high-value assets can be restructured into Q&A formats that mirror how patients and health assistants process information. For instance, turning a 30-page clinical study into a structured page answering “how effective is atorvastatin for high cholesterol?”

PubMed metadata completeness is worth checking. Incomplete metadata remains the most common reason good studies stay invisible in clinical AI tools.

Consistent INN-brand pairing across all indexed content also helps AI models learn to associate the two rather than defaulting to the scientific name.

Build governance within 1 year. The monitoring perimeter needs to expand from what you publish to what AI says about you, across generalist LLMs, clinical AI tools, and health assistants.

Quarterly testing on product claims, indications, and safety data helps detect drift early. It’s useful to define escalation thresholds, for instance, an AI recommending a blood thinner at twice the approved dosage should trigger an immediate review by Medical Affairs and Regulatory.

It’s equally useful to classify content by level of AI visibility. Some are safe to make fully discoverable such as your approved label, your published clinical trials. Some can be indexed but needs careful framing, like real-world evidence that could be misinterpreted without context. And some should stay out of AI reach entirely such as ongoing trial data or pre-approval results that aren’t ready for public interpretation.

Organizations that build this framework now are likely to be ahead when regulation solidifies, rather than reacting to it.

Conclusion: The narrative is already being written

AI visibility in healthcare is not a trend, it is an infrastructure shift.

  • Citation patterns, once established, compound: first movers build an advantage that latecomers will pay to close.
  • RAG-based clinical tools will become the dominant interface for specialists within three years.
  • The Utah model may expand globally, permanently compressing the GP touchpoint.
  • Dedicated health assistants are creating a permanent new intermediary between the patient and the physician, and between the brand and the prescriber.

Where to start: follow the influence chain, not the end user.

  • For prescription products, the physician remains the decision point. Ensuring your clinical evidence is findable in RAG platforms, generalist LLMs, and health assistants is the most direct lever on prescribing behavior available today.
  • For OTC products, AI is replacing the shelf as the first point of product discovery, and if the answer says “cetirizine 10mg” instead of your brand, the patient buys the cheapest generic. In a category built on volume and brand premium, that’s how market share disappears.

Depending on your portfolio, these two tracks can run in parallel or be sequenced, but neither should wait.