Responsible AI in Medical & Regulatory Writing: Why Expert Oversight Matters
Generative AI tools are starting to revolutionize how many industries engage with regulatory obligations. Notwithstanding the known limitations of current models, many companies and government entities have made the decision to integrate AI platforms into their processes in order to cut costs and improve efficiency.
Adopters of these tools allege that they can gather background information, draft outlines, and spot gaps better or more efficiently than unassisted humans. Yet the very speed and fluency that make these systems attractive can also mask serious risks, especially when an unverified draft migrates into a regulated submission or scientific manuscript.
Over the past two years a series of public missteps has underscored the dangers of relying on AI output without rigorous human review:
- Legal briefs with phantom precedents. Two New York attorneys and, more recently, a Utah lawyer were sanctioned after ChatGPT "cited" court decisions that did not exist, leading judges to levy fines and strike the filings (reuters.com, theguardian.com).
- Political health reports citing fake studies. A campaign white paper championed by Robert F. Kennedy Jr. lifted AI-generated references that could not be found in any journal, passing misinformation directly to the public (poynter.org).
- A surge of low-quality biomedical papers. Nature recently warned that AI-assisted manuscripts are flooding journals with misleading health claims, forcing publishers to retract record numbers of articles (nature.com, retractionwatch.com).
These examples matter for medical and regulatory writers because they illustrate a central truth: Large language models can imitate scientific tone but do not guarantee scientific truth. Citations, statistics, and even study designs may be "hallucinated,", and any error that slips into an Investigational New Drug (IND) or Clinical Evaluation Report (CER) could derail approval timelines or, worse, patient safety.
How AI Hallucinations Happen and Why They Are Hard to Spot
- Training on imperfect data. AI models are only as trustworthy as the data they ingest. If retracted or low-quality studies are in the corpus, spurious claims will surface. A Memorial Sloan Kettering analysis showed popular AI search assistants rarely flag retracted trials (library.mskcc.org).
- Fictional but plausible citations. Because language models optimize for coherence, they can combine journal titles, authors, and page ranges into citations that look authentic but are completely fabricated.
- Confident prose that masks uncertainty. LLMs rarely express probability. An incorrect statement is delivered with the same polish as a correct one, making quick visual checks ineffective.
The Human Safeguard: Hiring the Right Medical Writer
Technologists often frame AI as a replacement for writers. At GLOBAL, we view it as a potential force multiplier, but only in the hands of experienced professionals who can be trusted to audit and refine its output. Furthermore, we believe that AI should never be used without full disclosure, a description of methods, and a justification and ensuring that all appropriate safeguards exist to protect proprietary data. The core competencies outlined in our earlier article on "Hiring the Right Medical Writer" become even more critical in the AI era:
Choosing a Responsible Partner in the Age of AI
In today’s environment, almost anyone can use AI to create text that sounds knowledgeable. Unfortunately, that surface-level polish can give a false sense of expertise. An organization with limited regulatory experience can appear credible simply by prompting a generative tool and presenting the results as their own. This is particularly dangerous in regulated industries, where a misstatement or misinterpretation can delay approvals, damage reputations, or jeopardize patient safety.
The most serious risk, however, is that incorrect or fabricated information may slip past internal reviewers and make its way into a submission, similar to what we have already seen in other contexts. When that happens, the consequences can be severe: regulatory delays, public retractions, loss of credibility with authorities, and even harm to patients. With AI-generated content, the margin for error becomes thinner.
If you are searching for a medical writing vendor, ask how they use AI, whether they disclose it, and what checks are in place to ensure quality and compliance. A trusted regulatory writing firm will not only be transparent but will also demonstrate a structured review and validation process behind every deliverable.
Best-Practice Workflow for Responsible AI Use
- Define the question first. Writers draft a tight prompt or outline grounded in reliable, citable data sets.
- Let AI assist, but never decide. Use tools for first-pass language generation or gap analysis, but quarantine drafts until verification is complete.
- Apply AI to limited, specific questions. Avoid using it to answer broad or blanket questions where the origin of data is unclear. Ask narrow questions and check all assumptions and cited information immediately.
- Identify and check every citation. Cross-reference PubMed IDs, DOIs, cited articles, clinical-trial registries, and the Retraction Watch database before accepting a reference.
- Document the audit trail. Maintain version control and rationale for every change to satisfy internal QA and external regulators.
Utilize Great Writers to Ensure Quality Submissions
Our team of experienced regulatory writers and consultants is here to provide the support you need. Whether it’s regulatory submissions, project management, or individualized training we deliver high-quality, compliant content to keep your projects on track. Let us be your trusted partner—ensuring clarity, precision, and expertise in every deliverable.
Contact us today to learn how our writing and consulting services can support your team!
