AI-Generated Health Content: What Review Looks Like Now

I can ask a large language model to write an article about a blood pressure medicine and have a clean, structured, confident draft before I've finished my coffee. It will have an introduction, neat subheadings, a balanced-sounding tone, and a tidy conclusion. It will read like something a competent health writer produced on a good day. And somewhere in it — often in the part that sounds most assured — there will be a dose that doesn't exist, a guideline that was superseded two years ago, or a missing line that, in a real patient, is the difference between a routine day and an emergency.

This is the new shape of the problem. AI has not made medical review obsolete. It has changed what the review is for — and, if anything, made it more necessary, because the thing producing the content is now extraordinarily good at sounding right.

What AI genuinely does well

Credit where it's due. Language models are excellent at the things that used to consume an editor's time: structure, flow, plain-English explanation, turning a tangle of facts into a readable order. They rarely produce gibberish. They are tireless, fast, and often clearer than the humans they assist. For the mechanical craft of writing, they are a real tool.

That competence is exactly what makes them dangerous in this domain. A clumsy error announces itself. A fluent error hides. When the prose is this good, the reader's natural quality signals — does this sound like it knows what it's talking about? — stop working, because it always sounds like it knows.

What AI characteristically gets wrong

The failures are not random. They cluster in recognisable ways, and naming them is most of the reviewer's new job.

Confabulated specifics. The model generates the most plausible-looking answer, which is not the same as the correct one. Ask for a dose, a statistic, a trial name, a citation, and it will often supply something with the exact shape of a real fact — a number in the right range, a study with a credible-sounding title — that is simply invented. These are the hardest errors to catch precisely because they look like the parts you'd normally trust. A vague claim invites scrutiny; a confidently specific fabricated number does not.

Outdated guidance. A model's knowledge is anchored to its training data, and medicine moves faster than training cycles. It will cheerfully state a threshold, a first-line treatment or a safety position that was current once and has since been revised. It has no internal sense that guidance has a date, so it presents superseded advice with the same confidence as live advice.

Fluent overconfidence. Models hedge poorly in the ways that matter. They will state contested questions as settled, smooth over genuine uncertainty, and rarely volunteer "this is debated" or "the evidence here is weak" unless specifically pushed. The tone is uniformly assured regardless of how solid the underlying claim is — which is the opposite of how good clinical writing signals reliability.

Missing safety-netting. Unless instructed, a model optimises for a complete, satisfying answer — not for the cautious "and if this happens, seek help today" that a clinician adds by reflex. It will describe a condition or a medicine thoroughly and never mention the red flags, because nothing in its training rewards the unsatisfying, life-protecting caveat.

No sense of clinical consequence. This is the deepest one. A model has never watched what happens when the missing interaction was needed. It has no mental library of the people who acted on a sentence and came to harm. It treats every claim as text of equal weight, with no instinct that this line is decorative and that line is the one that, got wrong, sends someone to hospital. It cannot triage its own output by danger, because it has no concept of danger.

How the job changes: from prose-fixing to claim-auditing

Put those failure modes together and the reviewer's work shifts on its axis. With human-written content, a large part of review was improving the writing — clarity, structure, tone. AI hands you that for free. What it cannot hand you is verification, and so the job moves from fixing prose to auditing claims.

In practice that means treating every specific as unverified until checked: every dose against a current formulary, every threshold against present guidance, every named study against its actual existence and findings. It means reading not for what's wrong on the page but for what's plausibly fabricated and what's conspicuously absent. The fluency that used to signal competence becomes a thing to read past. Paradoxically, a rough human draft is sometimes easier to review, because its weaknesses are visible rather than camouflaged.

Disclosure and accountability

There is a second layer beyond accuracy, and it is about honesty with the reader. Health content increasingly passes through AI somewhere in its making, and editorial standards are catching up to the question of when that should be disclosed and who remains accountable for the result. The principle that survives the technology is simple: a named, qualified human takes responsibility for what is published, regardless of what drafted it. A model cannot be accountable — it cannot be struck off, cannot be sued, cannot stand behind a claim. "The AI wrote it" is not a defence; it is a description of a process that still needs a person at the end of it.

Used well, these tools are a genuine asset — a fast, capable drafting partner that frees a clinician to spend their attention on exactly the claim-checking and safety judgement only they can do. The danger is not the tool. It is publishing its output as though fluency were verification.

Practical takeaways

• AI excels at structure, flow and plain explanation — which is precisely why its errors are so well camouflaged.
• Its characteristic failures are predictable: confabulated specifics, outdated guidance, fluent overconfidence, missing safety-netting, and no sense of clinical consequence.
• Confidently specific invented facts — a dose, a statistic, a citation — are the hardest errors to catch and the most worth hunting for.
• Review shifts from improving prose to auditing claims: verify every specific, watch for what's absent, and read past the fluency.
• A qualified human stays accountable for published health content, whatever drafted it — "the AI wrote it" is not a defence.

What this doesn't mean

It doesn't mean AI has no place in health writing, or that AI-assisted content is inherently untrustworthy. Used as a drafting tool under genuine clinical review, it can be excellent — faster and often clearer than the alternative. The argument is about sequence and responsibility: generation is not verification, fluency is not accuracy, and a model's confidence carries no information about whether it's right.

A closing thought

The old worry about health content was the obvious quack — the page that looked wrong and was. The new worry is the page that looks completely right and is subtly, fluently, confidently wrong, produced in seconds, at scale. That is a harder problem, and it does not get solved by better writing, because the writing is already good. It gets solved by someone who knows which sentence is load-bearing and is willing to check it — which turns out to be the same job it always was, with the easy part automated away and the hard part left entirely intact.

Further reading and sources

• Patient Information Forum — PIF TICK criteria, including emerging guidance on AI in health information
• International Committee of Medical Journal Editors (ICMJE) — recommendations on authorship, accountability and AI disclosure
• MHRA — Drug Safety Updates (the currency benchmark a model cannot keep pace with)
• NICE — guidance library, for verifying current UK clinical positions
• Peer-reviewed literature on large language model accuracy and confabulation in clinical contexts