Patient-Ready, PubMed-Steady: Why Medical AI Content Can’t Be “Just Marketing”

AIO, AEO, GEO: What These Acronyms Really Mean in Health and Pharma

When people talk about “AI and search” today, three acronyms appear everywhere: GEO, AEO, and AIO. They are often used interchangeably, but they do not mean the same thing—and in medicine, that difference matters.

AEO (Answer Engine Optimization) is about ensuring your content can be turned into a direct answer: clear questions, clean structure, definitions, thresholds, and red flags. It focuses on how search engines and AI features extract a short, safe summary from longer texts.

GEO (Generative Engine Optimization) goes one step further: it examines how generative systems (AI search, chat-based engines, overviews) assemble and remix information to generate new answers. Here, the goal is to give these systems a stable, evidence-based narrative they can reuse without drifting away from the facts.

AIO (Artificial Intelligence Optimization), in health and pharma, must go beyond visibility. It is not only “how often AI mentions you”, but how accurate, guideline-aligned, and medically defensible those mentions are when a patient or a doctor acts on them.

In this article, I look at AIO for health and pharma through three lenses:

• Logos – is the medical logic sound? Are numbers, risks, and recommendations traceable to real evidence?

• Ethos – who is speaking? Can a clinician, a regulator, or a medical affairs team stand behind this answer?

• Pathos – how does it land for a real human being who is worried, in pain, or simply confused?

My own bias is simple and transparent:
I have spent 30 years in dermatology, 20 years working with lasers, around 2+ years deeply involved in AI, and more than 15 years communicating in public, including being active on LinkedIn since 2006. I have seen medical content as a clinician, as an educator, and, more recently, as something AI will quote at 3 a.m. when no doctor is in the room.

From that perspective, AIO for health and pharma cannot be “just marketing”. It has to produce answers that are:

• clinically consistent over time,

• safe to reuse across models and channels,

• and human enough that patients can actually understand and act on them.

When “AI for content” quietly goes wrong in health and pharma

Most problems with “AI content” in medicine do not start with the model. They start much earlier, with the way we write, sign, source and update the information that AI will later reuse. By the time a wrong or incomplete answer appears in a chat window, the damage is already done: the system is only amplifying weaknesses that were present in the original content.

In health and pharma, this is more than an aesthetic issue. Anonymously written pages, numbers with no source, outdated terminology or oversimplified explanations can all be ingested by multiple AI systems and then repeated, remixed and quoted to patients and doctors for years. What looks like “just a website” today can become the backbone of thousands of future AI answers.

Instead of asking “Which AI tool should we use?”, a safer starting point is a different question: “Who is allowed to speak for us in front of AI?” The checklist below is one practical way to test any potential partner who claims to work on AI, content or “optimisation” in health and pharma. If they fail on these basics, the risk is not that you will be invisible in AI answers. The real risk is that you will be visible for the wrong reasons.

How to audit any “AI content” partner in health and pharma

If you strip away the buzzwords, choosing a partner for AI and medical content becomes a due diligence exercise. The safest way is to ask a few simple, brutal questions and watch how precise the answers are. The seven questions below are not about tools or dashboards. They go to the core: who writes, what they claim, how they prove it, and how AI will reuse it over time.

1. Who signs it? And when was it last reviewed?

Question to ask a partner

“Do you clearly show who authored the medical content, their credentials, and when it was last updated or reviewed?”

Typical red flags

• No author name or generic “Team” signature.

• No medical credentials attached (MD, PhD, specialist, etc.).

• No publication date and no “last reviewed” date.

Why is this dangerous

• In medicine, anonymous + timeless content = no accountability.

• In AI, models don’t see “we meant well”; they see text with no clear signal of authority or freshness. That can dilute or distort how LLMs weigh that source versus current, signed, guideline-based content.

Potential effects of this red flag

• Outdated treatment advice circulating through AI answers (e.g., obsolete protocols, old risk–benefit balances).

• Medical, legal, and regulatory teams cannot stand behind the content because there is no clear responsible author.

• LLMs may keep quoting content that is clinically obsolete but still online, which increases the risk of patient misinformation.

• In a crisis or safety signal, you have no clear audit trail: who wrote what, when, and based on which evidence.

2. Do the numbers come with proof, or just “sound good”?

Question to ask a partner

“Whenever they use numbers, risks, or percentages, do they provide clear links to original evidence (PubMed, guidelines, official reports)?”

Typical red flags

• “70% of patients improve…” with zero reference.

• “Studies show that…” with no citation, no PubMed ID, no guideline.

• Infographics and stats with no legend and no source.

Why is this dangerous

• Numbers without sources are marketing, not medicine.

• In health & pharma, every percentage can impact perception of efficacy, safety, or risk.

• For AI, unreferenced numbers become “floating facts” that LLMs may repeat without any grounding in real evidence.

Potential effects of this red flag

• Patients and HCPs often over- or underestimate risks and benefits, leading to misperceptions and incorrect decisions.

• Misinformation is “baked into” the AI ecosystem: once a wrong number appears in enough places, it can be reinforced by multiple models.

• Regulatory red flags: claims may be interpreted as promotional, off-label, or misleading if they cannot be traced back to valid evidence.

• In the worst case, harmful self-medication or refusal of indicated treatments based on fake “statistics”.

3. Is it written for patients – without losing scientific discipline?

Question to ask a partner

“Can they produce content that a real patient can understand, while staying strictly aligned with medical evidence and guidelines?”

Typical red flags

• Overloaded with jargon: reads like a paper, not a patient explanation.

• The opposite: oversimplified “storytelling” with no nuance, no caveats, no red flags.

• No separation between what is known, what is uncertain, and what needs a doctor’s decision.

Why is this dangerous

• If it’s too technical, patients will go elsewhere (often to low-quality sources).

• If it’s too “friendly” and vague, patients can misinterpret serious red flags as minor issues.

• AI models trained on this content may generate answers that sound empathetic but are medically unsafe or incomplete.

Potential effects of this red flag

• Patients misunderstand symptoms, red flags, and follow-up steps, which can delay or prevent necessary medical care.

• HCPs lose trust in the brand, perceiving its materials as either “too academic” or “too fluffy”.

• LLMs propagate advice that looks “caring” but misses critical exclusions, contraindications, or warning signs.

• Increased risk of complaints and medico-legal exposure: “the brand made it sound harmless”.

4. Do they use current AI terminology or outdated buzzwords?

Question to ask a partner

“Are they using up-to-date, correct names for AI products and features, or do you see outdated, experimental, or retired terms in their materials?”

Typical red flags

• They keep referencing old product names, discontinued experiments, or outdated features (e.g. using legacy labels long after official rebrands or product changes).

• They mix up product categories (LLM, search feature, lab experiment) as if they were all the same.

Why this is dangerous

• Outdated naming is a proxy for outdated understanding.

• If they don’t track changes in AI platforms, they are unlikely to understand how those platforms currently ingest, index, or surface medical content.

• Health & pharma need partners who can anticipate shifts in AI behaviour, not those stuck in last year’s pilot vocabulary.

Potential effects of this red flag

• Strategies built for surfaces or behaviours that no longer exist, wasting budget and time.

• False promises: “we optimise for X” when X is not even a live product anymore.

• Misalignment between internal expectations and what AI actually does, leading to frustration and distrust in “AI projects” overall.

• Long-term: your brand becomes associated with obsolete practices while your competitors adapt to the real AI landscape.

5. Can they explain, in plain language, what AI will actually do with your content?

Question to ask a partner

“If you ask them what happens to your text after you publish it, can they clearly explain the difference between training, crawling, retrieval, and citation?”

Typical red flags

• Vague answers like “AI takes everything from the internet” or “it will just learn from your site”.

• No distinction between:

  • content used to train models,

  • content used for live search and retrieval,

  • content used in summaries, overviews, and chat answers.

• No mention of robots/LLMs/crawling policies, or how different AI systems respect them.

Why this is dangerous

• Health & pharma cannot afford partners who treat AI as a black box.

• Without understanding how content flows through:

  • crawlers,

  • indexes,

  • ranking,

  • generative layers, they cannot design strategies that control risk and maximise safe exposure.

Potential effects of this red flag

• Over-sharing or under-sharing: either excessive blocking (losing visibility) or blind openness (uncontrolled reuse in AI answers).

• Inability to respond when new AI features launch: no clear model of “where in the pipeline we can act”.

• Missed opportunities to become a trusted, citable source across AI systems, because no one engineered that path.

• Failure to answer critical internal questions: “Why did AI say this about our product?” – because no one mapped input → pipeline → output.

6. Can they show at least one conceptual example of “patient-ready, science-backed” content?

Question to ask a partner

“Can they show you, even on a hypothetical topic, how they would write a patient-facing explanation and attach real scientific references to it?”

Typical red flags

• They only show:

  • landing pages,

  • slogans,

  • “tone of voice” decks, but no concrete medical content with evidence.

• When pushed, they show generic AI-generated text with no PubMed / guideline backbone.

• No structure like: question → explanation → caveats → when to see a doctor → references.

Why this is dangerous

• In health & pharma, the unit of value is one good, safe answer – not just pretty pages.

• If they can’t even demonstrate, on a fake case, how to blend clarity with depth, they will not manage real topics under real constraints (SmPC, PI, local regulations).

Potential effects of this red flag

• You end up with “AI content” that is indistinguishable from generic health blogs – exposing your brand, not protecting it.

• Medical/legal/regulatory pushback on every piece because they see no method, only improvisation.

• AI models ingest and reuse this low-discipline content, making it harder to push a consistent, guideline-aligned narrative later.

• Loss of trust: both internally (teams stop using the content) and externally (HCPs see it as superficial).

7. What does “success” mean for them: more clicks, or fewer mistakes?

Question to ask a partner

“When you ask them how they measure success, do they only talk about traffic and engagement, or do they also talk about error reduction and alignment with medical reality?”

Typical red flags

• The KPI list is purely marketing:

  • visits,

  • impressions,

  • time on page,

  • likes, shares, comments.

• No mention of:

  • how often AI answers about your topic are accurately,

  • how often the brand is mentioned correctly,

  • how closely answers follow guidelines and approved indication.

• No plan for monitoring or correcting AI answers over time.

Why this is dangerous

• In a YMYL domain, a viral but wrong answer is worse than no answer.

• If your partner only optimises for volume, not accuracy, they are misaligned with the real risk profile of health & pharma.

• AI will amplify whatever it finds: if the input is optimised for clicks, not correctness, you get high-reach, high-risk misinformation.

Potential effects of this red flag

• High “visibility” combined with low clinical reliability → perfect setup for reputational damage.

• AI answers that contradict guidelines or approved labels, creating confusion for both patients and HCPs.

• Escalation of complaints, safety reports, and regulatory scrutiny: “What you put online is fueling AI to say X about your product.”

• Internally, medical and regulatory teams lose trust in all AI initiatives, blocking future innovation.

AIO for Health & Pharma in a Multi-Model, Multi-Generation, Multi-Channel World

Multi-generational: one clinical truth, different ways of hearing it

A medical answer that enters the AI ecosystem will be read very differently by each age group. Teenagers want fast, direct, mobile-first explanations (“Is this serious? Can I go to school tomorrow?”). Young adults often look for compatibility with lifestyle (“Can I still work out, travel, drink alcohol?”). Middle-aged patients focus on risks, long-term control, and impact on work and family. Older patients care about safety, interactions with existing treatments, and who is taking responsibility. On top of that, doctors and other healthcare professionals read the same content through a different lens: “Is this aligned with guidelines and labels?”

AIO for health and pharma means writing one medically consistent explanation, then structuring it so that each generation can extract what it needs—symptoms, next steps, red flags, treatment logic—without losing the underlying clinical discipline.

Multi-model: different AI systems, one evidence-based spine

Medical content is no longer read by a single search engine. The same text can be ingested, indexed, or summarized by several large AI systems in parallel (for example, general-purpose chatbots, AI search engines, and clinical decision tools). Each model has its own way of:

• finding information (web crawling, documents, structured data),

• deciding what looks credible,

• compressing long evidence into short answers.

If every page tells a slightly different story about the same drug, device, or condition, each model will learn and reproduce a different version of “truth”. AIO for health and pharma, therefore, focuses on building a single, stable evidence spine—clear definitions, consistent wording, transparent references—that any model can quote or summarize without drifting away from current science and approved use.

Multi-channel: the same message, everywhere patients and clinicians look

Patients and clinicians will not meet your medical story in one place. They will see fragments of it in AI chat answers, classic search results, clinic websites, patient leaflets, professional portals, conference materials, and sometimes even in social media posts summarising studies or guidelines. If each channel improvises its own explanation, contradictions appear: different numbers, different thresholds to see a doctor, different descriptions of benefit versus risk.

AIO for health and pharma treats all channels as different doors to the same room. The core answer—what the condition is, what matters clinically, when to seek care, how a therapy should be used—is defined once, in a medically robust way, then adapted in length and format for each channel. The goal is not more stories, but one coherent, guideline-aligned story that stays the same whether it is read on a phone screen, in a waiting room, or inside an AI-generated summary.

In the end, it’s not about AI. It’s about the sentences you are willing to own.

In health and pharma, you do not need more AI promises. You need fewer unexamined sentences. Every anonymous paragraph, every number without a source, every oversimplified “friendly” explanation is a liability waiting to be amplified by systems you do not control.

Tools will change. Models will change. Interfaces will change. What does not change is this: somebody will be held responsible for what patients and doctors do after reading an AI-generated answer that mentions your name.

That is why these seven questions are not a “nice-to-have checklist”. They are a minimum safety protocol. If a potential partner cannot pass them, the problem is not their AI stack. The problem is their standard.

In a YMYL world, your content should be:

Patient-ready. PubMed-steady.
Easy to read. Impossible to mislead.
No mistakes. For you. For science.

Victor Gabriel Clatici, MD
Dermatologist · LLM Nutritionist · AI in Health & Pharma
Bucharest, Romania — November 23, 2025

The article was published first on LinkedIn.