AI in Regulatory: Lifeline or Liability for RA/QA Departments

How AI Is Transforming Regulatory Affairs and Quality Assurance in Medtech

Artificial intelligence moved quickly from consumer novelty to standard workplace equipment. After large language models became openly accessible in late 2022, almost every knowledge-based discipline began to test predictive text and pattern recognition. Regulatory Affairs and Quality Assurance teams were no exception. Some specialists pictured a morning in which software would draft the entire technical file and they would simply add a signature, whereas others warned that automated tools might push seasoned reviewers out of the profession.  

 Three years later, neither expectation has come true, yet the technology is already entrenched. AI now screens incident reports, searches literature, drafts preliminary risk statements, and flags inconsistencies faster than any spreadsheet macro. The discussion has therefore shifted from whether RA and QA departments should use AI to how they can employ it without compromising patient safety or product quality. 

AI in Vigilance Reporting and Risk Assessment

Early experience shows substantial benefits when machines assist skilled reviewers. Duplicate detection engines compare hundreds of vigilance reports or scientific abstracts within minutes and highlight near identical records that manual checks often miss. Large language models extract sample sizes, confidence intervals, and study endpoints from clinical trials that span several hundred pages, sparing engineers hours of table construction for every investigation.

One EU medtech manufacturer reported that automated content extraction cut screening time for complex clinical papers from roughly 30 minutes to about 15 seconds.  

 Complaint triage classifiers read free text remarks from users and assign them to regulatory categories such as nonconformity, medical device incident, or serious adverse event while adding a confidence score that dictates how much manual scrutiny remains necessary. Taken together, these point solutions reduce clerical workload, shorten feedback loops, and free scarce expertise for decisions that demand professional judgment. Across several midsized device portfolios, aggregated AI driven adverse event evaluation workflows have reduced manual workload by up to 70 percent. 

Three broader forces magnify the impact of those individual gains. First, model capacity and training data still follow an exponential curve. A capability that looks experimental in one product cycle often becomes a commodity feature two years later. Second, developers now build agent based frameworks that link reasoning steps with secure document retrieval, scheduling, and version control. Instead of returning a single sentence, the system can collect references, draft a summary, populate a template, and hand a complete package to a reviewer. Third, automated analysis restores a sense of scale. A person who needs three months to read three thousand surveillance files can receive a structured overview in less than an hour and then decide where detailed investigation is worthwhile. Speed, breadth, and repeatability therefore change team capacity rather than only convenience. 

Early Use Exposed Significant Challenges

The same period has exposed important shortcomings. Hallucination remains the most visible fault. A language model may invent a study title, list plausible authors, and supply a digital object identifier that resolves nothing. When the topic involves patient safety, invented literature is more than an irritation because it can hide genuine adverse event signals behind fabricated noise. Model drift is another concern. Continuous finetuning on uncontrolled data may erode accuracy, and the decline can remain hidden until an audit fails. Classification bias is subtler but no less serious. Algorithms trained on skewed data can underestimate harm in underrepresented populations or overreport minor issues that dominated the historical record. Errors considered trivial in consumer applications become unacceptable when they lead to missed incidents, delayed recalls, or flawed submissions. 

Regulatory Readiness: What to Look for in AI Validation

Because the margin for error is narrow, patient safety remains the primary benchmark for every adoption decision. Legal instruments such as the EU AI Act, the draft FDA guidance on good machine learning practice, and ongoing ISO work on AI management systems define baseline duties, yet none can guarantee the reliability of a specific product. Responsibility stays with the organisation that puts a model into service. Before purchase, buyers should confirm that the vendor maintains audited design controls, tracks dataset lineage, and publishes full version histories. They should also verify that every recommendation can be inspected and, when necessary, overruled. The software must disclose which features rely on statistical inference and which use deterministic code. Finally, the supplier should provide peer reviewed evidence of sensitivity, specificity, precision, and recall measured on external validation sets that reflect the class imbalance found in real postmarket data. 

How to Validate AI Tools Correctly

A defensible validation plan resembles device development. The team begins by assigning a risk class to each intended use and then defines numerical acceptance criteria. A triage function that filters life threatening incidents may demand at least ninety nine percent sensitivity with a narrow confidence interval, whereas an internal formatting helper can tolerate lower thresholds. Engineers curate a representative dataset, remove bias, and partition records into training, tuning, and locked test subsets with documented traceability. Cross validation exposes overfitting, and stress tests insert borderline and adversarial samples to gauge robustness. After technical verification, domain experts review a statistically significant sample of AI decisions, especially those that differ from earlier human verdicts. Release packages must include frozen model weights, test results, and a plan for monitoring key performance indicators once the system is live.

Balancing Certainty and Error in AI Output

Validation brings an inevitable question: how precise is precise enough? No technology or professional achieves perfection. Meta analyses show that neurologists misdiagnose Parkinson disease in roughly half of first consultations. If the human benchmark is that low, demanding absolute accuracy from a program could postpone tools that still reduce harm. A practical rule is that the algorithm should at least match the proven performance of a qualified reviewer under similar conditions and, when feasible, exceed it by a risk proportional margin. The threshold belongs in the risk management file and must be revisited whenever new evidence becomes available so that acceptance levels rise with growing datasets and tighter statistics. 

Successful adoption also needs organisational preparation. Staff require structured training that explains strengths, limits, and escalation paths. Quality records must store algorithm versions, validation reports, and links to underlying datasets. Standard operating procedures must spell out when a person must intervene and how to document overrides. Ethics committees should confirm that the system does not introduce bias against protected groups and that personal data remain secure. Information technology teams must supply robust audit trails and make sure that retraining only occurs under change control. Governance meetings that involve regulatory, clinical, IT, and data science staff help ensure that lessons from postmarket surveillance lead to timely model updates rather than adhoc patches.

Endnote: Use AI, but Do So Carefully

A balanced path forward is both possible and necessary. Ignoring AI would lengthen reviews, raise transcription costs, and widen the gap between internal processes and the methods used by notified bodies and competent authorities. Blind adoption, however, would jeopardise patient safety and corporate credibility. The sensible approach is to treat every AI feature as a component that influences device safety, apply the same validation discipline that governs medical hardware and software, and monitor the tool for its entire life cycle. When organisations follow that path, clerical labour declines, feedback loops shorten, and specialists regain time for the complex scientific judgments that no algorithm can yet replicate. 

Disclaimer. The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the official policy or position of Test Labs Limited. The content provided is for informational purposes only and is not intended to constitute legal or professional advice. Test Labs assumes no responsibility for any errors or omissions in the content of this article, nor for any actions taken in reliance thereon.