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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.

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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.

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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.

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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 claim verification, 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.

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Selected writing

A Day in the Life of an MSL Powered by AI: Combining AI Technologies to Transform Training — Journal of Next-Generation Research 5.0. A conceptual example of how RAG, generative AI, multimodal systems, and AI agents combine to support pharmaceutical workflows.How to Break a Large Language Model — AI Advances. On the ways large language models fail under stress and ambiguous prompts.Translation, not Interpretation: Rethinking Language Model Design for Healthcare — a deeper discussion of why healthcare AI systems should focus on translation rather than interpretation.
AI tools will keep changing. The fundamentals of good medical writing will not: clear evidence, careful interpretation, and responsible communication.
Last reviewed: 15 April 2026 · 2 min read