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Dr. Rachel Benett
September 30, 2026
Recently I was asked to review a deviation investigation before it went to the quality unit for approval. The root-cause narrative was fluent, well structured, and supported by a citation to a specific paragraph of a regulatory guidance document. The paragraph did not exist. The investigator had asked a general-purpose artificial intelligence (AI) assistant to tighten the wording, and the tool had done what such tools do when they meet a gap: it produced a plausible reference to fill it. In any GxP-regulated setting, an auditor would cite that one unverified sentence; in a pharmacovigilance record it can compromise data that patient safety decisions depend on. That review changed my view: anyone who touches a regulated document should learn how these tools fail before they rely on them.
Neither the United States Food and Drug Administration (FDA) nor the European Medicines Agency (EMA) has written a rule that says “do not use ChatGPT”. What both agencies expect is that the organization remains accountable for every record it creates, whatever tool helped to create it. Under Part 11 of Title 21 of the Code of Federal Regulations (21 CFR Part 11), the FDA expects electronic records to be validated to ensure accuracy and reliability, and the EMA reflection paper on AI in the medicinal product lifecycle, adopted in September 2024, places responsibility for any AI-assisted output on the applicant or marketing authorization holder. An assistant that drafts or summarizes documents therefore falls under the same quality system controls as any other process step, and inspectors are entitled to ask how you verified its output.
The most useful thing I teach regulated teams is also the least glamorous: a large language model (LLM) does not look anything up; it predicts the next most probable words given the words before them. That is why the output reads so fluently, and also why it can produce a guidance clause, a journal reference, or a batch number that has never existed. The model is not lying; it has no concept of true or false. It is completing a pattern, and a confident citation is simply a very common pattern in the documents it learned from.
A fluent paragraph is not evidence of anything, and a reference is a claim to be checked against the source. The lawyers sanctioned in Mata v. Avianca learned this in June 2023, when a federal court fined them and their firm 5,000 dollars for filing a brief that cited non-existent cases generated by ChatGPT. A practical AI training course that begins with how the technology works, rather than with a list of prohibitions, gives your team the instinct to spot the red flags that fabricated content carries. The assistants will change with every release; the mechanism underneath them will not.
I teach from documented failures because each exposes a different failure mode. These five anchor every hands-on practical AI training session I run.
Together they describe the full arc of a regulated process: qualify the tool, protect the input, verify the output, own the result. Each failure traces back to a control that was missing: validation before use, verification before signature, monitoring after release, a rule about what enters a prompt, or clear ownership of the result.
The Samsung incident is the one that most often changes behavior. A pharmacovigilance associate pastes a case narrative into a public assistant to improve the English; a quality engineer asks a chatbot to summarize an investigation that names the batch and the supplier. Each action may transmit patient data and commercially confidential information to a third-party service whose retention terms the user has never read. In the European Union (EU), personal data in clinical trials and safety reports falls under the General Data Protection Regulation (GDPR), and the sponsor is generally the controller of that data regardless of which tool processed it. Our GDPR for Pharmaceuticals and Clinical Trials course covers that legal framework in depth; what practical AI training for regulated teams adds is the working discipline: knowing which tool is approved for which class of information, how to de-identify a case narrative, and when the correct answer is not to use the assistant at all. If you cannot demonstrate where the data went and who could access it, you cannot show that this step of your process is controlled.
Regulators on both sides of the Atlantic have described responsible use, and the common thread is human accountability. In January 2025 the FDA issued draft guidance on the use of AI to support regulatory decision-making for drugs and biological products, proposing a risk-based credibility assessment framework for a defined context of use. The EMA reflection paper expects human oversight proportionate to the impact of the AI output on patient safety and data reliability. A draft Annex 22 on artificial intelligence, added to the European Union (EU) Good Manufacturing Practice (GMP) guidelines for consultation in July 2025 and not yet finalized, would as currently drafted exclude generative AI and LLMs from critical GMP applications. Since 2 February 2025, Article 4 of the EU AI Act, Regulation (EU) 2024/1689, has required providers and deployers of AI systems to take measures that support AI literacy among their staff, a codified obligation that our AI Compliance in Life Sciences: EU AI Act and FDA Guidance course examines clause by clause.
None of these documents tells a medical writer how to phrase a prompt so that the summary stays faithful to the source. That is the working layer, and it is where practical AI training earns its place alongside governance training. Prompt with the source document, the audience, and the constraints stated explicitly, so the model has less room to fill gaps. Verify every claim, figure, and reference against the original before the output enters a controlled document. Govern by recording which tool was used, for what, and who reviewed the result, so the audit trail for an AI-assisted record is as complete as for any other data. A team that can demonstrate those three steps has an answer for the inspector; a team that cannot has a data integrity gap, whichever agency is asking.
Our two-hour online course was built for people who already use assistants at work and need to do so safely, quickly, and defensibly. Across ten hands-on lessons, seven step-by-step tutorials and the five real incidents discussed in this article, it teaches the whole working practice. This practical AI training for healthcare and pharma was built by a team of Regulatory Affairs Experts with qualifications from Northeastern University, Boston.
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The medical writer who drafts a clinical study report summary with an assistant needs it first, because a fabricated reference in a submission is found by an agency reviewer rather than a colleague. The clinical operations manager who compares protocol amendments with the same tools needs it for a related reason: a missed change in an inclusion criterion becomes a protocol deviation. The pharmacovigilance associate handling case narratives carries the heaviest confidentiality burden of the three, since the text they are tempted to paste is personal health data by definition. The quality assurance specialist reviewing deviations and Corrective and Preventive Action (CAPA) records, the person who caught the phantom citation in my opening example, needs to recognize AI-generated text on sight, and the line managers who approve that work complete the picture, because approval under GxP is a personal accountability. For each of these roles, certified practical AI training is the difference between using the tools and being able to defend having used them.
The productivity case for these tools is widely accepted, and adoption has outpaced governance. The 2024 Work Trend Index from Microsoft and LinkedIn, a survey of 31,000 people, found that 75 percent of knowledge workers used generative AI at work and that 78 percent of those users brought their own tools. Those figures describe workers in general rather than regulated teams, but they suggest that in many regulated organizations the assistants are already in use and invisible to the quality system. The choice is not whether to allow AI but whether the people using it have a working method. The return on investment (ROI) of practical AI training for your team is measured mainly in what does not happen: the inspection observation on an unverified reference, the breach notification after a case narrative reached a public server.
Two hours per person is a negligible cost next to the remediation a single one of those events triggers, and it produces something an inspector can hold: evidence that each user was trained on confidentiality, verification, and governance before the first prompt. With regulators on both sides of the Atlantic expecting accountability for every AI-assisted record, practical AI training is the audit-ready benchmark for 2026 teams that intend to keep the speed without inheriting the risk.
Every course in our catalog is developed by practicing regulatory affairs and quality professionals, so the content reflects how inspections unfold in practice. On completion, each client receives a unique, verifiable, CPD-CEU accredited certificate that any auditor can check online and that can be shared directly on LinkedIn, backed by twelve months of self-paced access so the material can be revisited as tools and guidance change. Managers gain progress tracking and human resources (HR) reporting tools that turn individual completions into a team training record, the content is refreshed monthly as the regulatory picture evolves, and every course is Sharable Content Object Reference Model (SCORM) compatible.
The investigator in my opening example was not careless; they were fast, and the tool was faster. The paragraph that did not exist was caught because one reviewer knew what a language model does when it runs out of facts, and I would rather that knowledge sat with the whole team than with one person. Prompt, verify, govern: those three steps keep AI assistants under the control of your quality system, and the shortest route to them is a certified practical AI training course completed before the next document is signed.
