About

​

Who wrote this

The Medical Writing AI Playbook was created by Nick Lamb, a medical writer and AI developer working at the intersection of healthcare communication and applied AI.

​

Background

Nick has more than 20 years of experience in medical communications, working across publications, medical affairs, and regulatory writing. Alongside that work, he has been building practical AI tools for the problems that show up repeatedly in medical writing — verifying claims against references, checking content for compliance issues, extracting structured data from source documents, and making complex medical information easier to understand.

​

Why this playbook exists

Most guides to AI focus on generic prompts, productivity tips, or tool lists. Medical writing needs something more specific. Evidence-based scientific communication depends on four things that general AI guidance rarely addresses:

• Source grounding — every claim tied to a specific document
• Accurate interpretation — numbers, endpoints, and populations reported as the source describes them
• Regulatory awareness — understanding what can and cannot be said in a given context
• Careful wording — the difference between “associated with” and “improves” is not cosmetic

This playbook was written to provide practical guidance for using AI across the full medical writing lifecycle — from evidence handling through drafting, adaptation, verification, and delivery — in a way that respects those constraints.

​

Informed by practical systems

Many of the examples, workflows, and failure modes described in the playbook are informed by real systems built for medical writing and healthcare communication — including tools such as RefCheckr for closed-loop claim verification and rewrite, MedCheckr for compliance review, and Patiently AI for plain language explanation. The aim of the playbook is not to promote any particular tool. It is to share the patterns — what works, what fails, and what deserves human judgement — that have emerged from building and using them in practice.

​

How this playbook is maintained

The playbook makes specific, dated claims about journal policies, regulatory positions and tool capabilities. Those claims go out of date. This is how they are kept current. Review cycle. Every page is reviewed at least quarterly. The “Last reviewed” date at the foot of each page is the real date of that review, not the date of the last typo fix. Event-driven updates. Pages are updated out of cycle when something material changes — a journal revising its AI policy, a regulation coming into force, a tool changing what it does. Two examples from 2026: JAMA Network moved three categories from “disclose” to “not permitted” in August, and the EU AI Act’s transparency obligations became enforceable on 2 August. Both were reflected within days. Sourcing. Factual claims link to a primary source — the publisher’s own policy page, the regulation, the agency guidance. Where a policy is likely to change, the playbook links to it rather than reproducing it, so a stale copy here cannot quietly contradict the real thing. Corrections. If something here is wrong, report it. Corrections are made openly and noted in the changelog — the full edit history is public in the GitHub repository. Version. The playbook is versioned, and every version is archived with a downloadable PDF. If you are working from a saved copy, the version and date are on its cover.

---

​

Disclosures

The playbook asks writers to declare AI use and disclose interests. It should hold itself to the same standard. Commercial interest. The playbook is written by the founder of PharmaTools.AI, and several tools referenced in it — RefCheckr, MedCheckr, Patiently AI, LLMentor, PLS Generator, PosterLens, PubCrawl — are PharmaTools.AI products. They are commercial. That is a real interest, and it is the reason those tools appear at the workflow steps where they fit. How that is handled. Workflow pages name third-party alternatives alongside the PharmaTools.AI options, and the tool ecosystem page covers the wider landscape regardless of who built it. The principles, risk framework, disclosure guidance and review standards are tool-agnostic and stand whether or not you use any of these products. Where a general-purpose model does the job, the playbook says so. Independence. The playbook receives no external funding and carries no sponsorship, advertising or paid placements. No pharmaceutical company, publisher, agency or AI vendor has reviewed, approved or paid for any of this content. Free and ungated. Every page is public. There is no login, no paywall, no email capture. The PDF is a direct download.

---

​

How AI was used to write this

Applying the disclosure template from this playbook to the playbook itself:

Claude (Anthropic) was used throughout the development of the Medical Writing AI Playbook — for research and source gathering, page structure and outlining, prose drafting, and for the repository tooling, build scripts and site configuration. All content was reviewed, edited and verified by Nick Lamb, who takes responsibility for the accuracy and integrity of the published material.

Some specifics, because vague disclosure is the thing this playbook argues against: Every factual claim was verified against a primary source by a human. No journal policy, regulatory date or tool capability appears here because a model asserted it. This matters most for references and citations, which are the failure mode most likely to embarrass you — nothing in this playbook cites a source that was not opened and checked. AI drafted; it did not decide. Risk tiers, review requirements, what belongs in the playbook and what does not, and every judgement about where AI should not be used are editorial decisions made by a person. The same limits apply here as anywhere. This playbook was produced with AI assistance, and it is not thereby exempt from the failure modes it documents. If you find a claim that does not hold up, that is exactly what the corrections process is for.

---

​

Using and citing this playbook

The playbook is published under CC BY 4.0. You can use, adapt and build on it — including commercially, and including inside your organisation’s SOPs and training materials — with attribution. Suggested attribution:

Adapted from the Medical Writing AI Playbook by Nick Lamb / PharmaTools.AI, licensed under CC BY 4.0. https://playbook.pharmatools.ai

Suggested citation:

Lamb N. Medical Writing AI Playbook. PharmaTools.AI; 2026. Available from: https://playbook.pharmatools.ai

---

​

Selected writing

---

AI tools will keep changing. The fundamentals of good medical writing will not: clear evidence, careful interpretation, and responsible communication.

---

Last reviewed: 20 August 2026 · 6 min read