Medical Writing and Generative AI: What Are the Limits and Best Practices?

Generative AI can speed up medical writing: first drafts of standard sections, layman’s summaries, and consistency checks. It does not replace the medical writer. If misused, it can invent references, misinterpret statistical results, or disclose confidential data. In a field where every sentence carries the sponsor’s liability, the question is not whether to use it, but how to manage it. Here is the approach we take in our medical and scientific writing projects.

How Can Generative AI Benefit Medical Writing?

Large language models are effective at repetitive and highly structured tasks. When used by an experienced writer, they free up time for scientific interpretation and discussion, which remain at the heart of the profession.

Usage	Interest	Alert Level
First draft of the descriptive sections	Saves time on ethics, organization, and the curriculum	Medium
Summary in plain language	Help with writing a patient-friendly summary	High
Consistency Check	Identify discrepancies between text and tables	Medium
Rewording and Translation	Consistent Style, Document Translation	Medium
Help with Bibliographic Categorization	Pre-classify article abstracts	High
Interpretation of the Results	None: subject to medical judgment	Not Recommended for Use

Generative AI has well-defined limitations

• Hallucinations. A model may generate a plausible but nonexistent bibliographic reference, or a figure that is not found in the source data. In a clinical study report, a single error of this type can undermine the credibility of the entire document.
• Confidentiality. Copying a protocol, patient data, or unpublished results into a tool intended for the general public may constitute a data breach. The GDPR and trade secret laws apply in full.
• A lack of statistical nuance. A model may confuse statistical significance with clinical relevance, or present a secondary endpoint as evidence of efficacy.
• Traceability. The principles of data integrity (ALCOA+) require knowing who produced what. A document generated without any trace of its origin poses a problem during an audit or inspection.
• Biases. Models replicate the biases present in their training data—for example, the underrepresentation of certain populations in the literature.

The regulatory framework is taking shape

There is not yet a specific document addressing AI in medical writing, but several guidelines already apply. In 2024, the European Medicines Agency published a discussion paper on the use of AI in the drug lifecycle. It reiterates that the sponsor remains responsible for all data and documents submitted, regardless of the tools used.

The European Artificial Intelligence Act (AI Act) establishes progressive obligations based on the risk level of the systems. In terms of publications, the ICMJE guidelines—followed by most medical journals—specify that AI cannot be listed as an author and that its use must be disclosed. Finally, the risk-based quality approach promoted by ICH E6(R3) naturally extends to the digital tools used in a clinical trial.

Best Practices for Using AI in Medical Writing

1. Ensuring human oversight. All generated content is reviewed, verified, and approved by a qualified medical writer. AI makes suggestions; the expert makes the final decision.
2. Use validated tools. Only tools deployed in a secure environment—where data is not reused for training—are permitted for confidential documents.
3. Verify every source. Every reference, every figure, and every quote is checked against the original document, without exception.
4. Document usage. An internal procedure specifies authorized uses, and the use of AI is tracked in the document’s file.
5. Train the teams. Writing a good prompt and identifying a hallucination are skills in their own right.

When strictly followed, these rules save time on formatting without compromising the content. They apply to all deliverables: CSR, investigator’s brochure, publications, or systematic literature reviews.

The medical writer remains at the heart of the system

AI is unaware of the study’s context, communications with regulatory authorities, or the sponsor’s expectations. It cannot resolve conflicts between two interpretations of a result, nor can it assume responsibility for a signed document. The field is evolving: less data entry, more verification, analysis, and methodological consulting. Teams that combine scientific expertise with proficiency in digital tools—as described in our article on the digitization of clinical trials—are gaining a competitive edge.

Rigorous Medical Writing with MultiHealth Group

At MultiHealth Group, our medical writers integrate digital tools within a clear quality framework, without ever delegating scientific judgment. Learn more about our expertise in medical and scientific writing and our digital tools, or contact our teams.

Frequently Asked Questions About AI in Medical Writing