What Big Pharma Is Really Telling Us About AI in Medical Writing

Over the past six months, conversations with senior medical writing and regulatory leaders across large pharmaceutical organizations have begun to converge around a clear and consistent message. Despite the rapid rise of Generative AI, enthusiasm inside regulated environments is becoming more measured. What is emerging is not resistance to AI, but a more mature, risk-aware understanding of where different technologies genuinely belong.

This shift closely mirrors the direction set out in the January 2026 FDA–EMA Guiding Principles of Good AI Practice in Drug Development, which emphasize human-centric design, risk-based use, clear context of use, and reliable, traceable outputs across the drug-development lifecycle.

In short: the conversation has moved on from whether AI should be used to how it can be used without breaking trust.

GenAI fatigue is driven by QC burden, not fear of innovation

Large pharma teams have actively explored GenAI for clinical and regulatory documentation. Many have run pilots. Some have deployed tools. Almost all report the same underlying problem that the QC burden is unsustainable. Senior leaders are increasingly explicit. Editing narrative for clarity or interpretation is acceptable. Verifying whether numbers are correct is not.

In practice, teams and recent evaluations report that off-the-shelf LLM-driven approaches deliver inconsistent performance in regulated pharmaceutical documentation tasks, such as clinical trial protocol generation. For example, assessments of leading models have shown strong results in areas such as content relevance, suitability, and appropriate use of terminology, but they fall short in critical clinical thinking and logic, resulting in outputs that are not 100% accurate and demonstrate inconsistent reasoning [1-3]. While such levels may be tolerable in low-risk domains, they are unacceptable for regulated documentation, where patient safety, inspection readiness, and traceability are non-negotiable.

This is why interest is shifting toward approaches that generate output directly from source data, behave deterministically, and eliminate the need to QC numbers altogether. This distinction aligns directly with the FDA–EMA emphasis on risk-based validation proportional to context of use, particularly where AI contributes to evidence generation. The issue is not a lack of innovation, but rather who bears the risk when the outputs are incorrect.

Lean medical writing is non-negotiable and poorly served by most tools

Another strong and recurring signal is dissatisfaction with the shape of AI-generated content. Many tools produce fluent, verbose outputs that look impressive but fail in practice. Senior leaders are clear that regulatory reviewers do not want bulk.

Across large organisations, internal discussions increasingly converge on the same conclusion. Lean medical writing is not optional. It requires:

• Minimal viable content

• Factual, data-driven interpretation

• Clear identification of clinically relevant signals

• Elimination of descriptive padding and narrative “fluff”

This challenge is amplified in organisations where therapeutic areas have historically written very differently. When verbose styles are imposed broadly or amplified by AI, the result is inconsistency, reviewer frustration, and documents that do not support decision-making. AI systems that generate more text rather than better judgement are increasingly seen as creating work, not removing it.

Deterministic approaches better reflect regulated reality

Importantly, this shift does not represent a rejection of GenAI altogether. Most senior leaders expect multiple AI technologies to coexist. What is changing is the consensus that high-risk, data-driven regulatory documents require deterministic behaviour.

Approaches that generate output directly from structured source data, apply consistent expert-defined logic, regenerate identically when inputs change, and clearly separate deterministic processing from generative assistance are viewed as fundamentally better aligned with regulated workflows than probabilistic, text-first drafting tools.

This reflects real operational experience. It also closely mirrors regulatory expectations around context of use, data governance, traceability, and lifecycle management for AI systems used in drug development. What is essential to understand is that not all AI belongs in the same risk class.

Human expertise must be amplified, not replaced

A further theme emerging from senior leadership is frustration with the assumption that AI should “replace” medical writers. In practice, the opposite is true. The most effective use of AI is where it removes mechanical, error-prone work, stabilises outputs across teams and vendors, and surfaces clinically relevant signals consistently, and leaves human experts to do what only they can do: apply judgement, interpret meaning, and shape the scientific story.

AI that ignores this human–technology relationship quickly loses credibility, regardless of how advanced the underlying model may be.

Trust, usability, and predictability matter more than novelty

One of the most telling insights from senior leaders is that the underlying technology matters less than whether it can be trusted and easily applied. What consistently matters is a single coherent user interface, clear understanding of what the system is doing, visibility into which outputs are deterministic versus generative, and confidence that outputs can be relied on without hidden risk. AI that requires users to second-guess, verify, or reverse-engineer its outputs rapidly erodes trust, no matter how innovative it claims to be.

A clear convergence is emerging

Taken together, the message from large pharma leadership is remarkably consistent, and it is increasingly echoed by regulators themselves. Quality control of numerical data remains a non-negotiable requirement. Current accuracy levels are simply not acceptable in regulated documentation. Lean medical writing is viewed as essential rather than optional. Deterministic, risk-based approaches are seen as far better suited to high-risk content. Above all, trust, transparency, and predictability are considered more important than technical novelty.

This signals a shift away from experimentation for its own sake and toward AI systems that can scale responsibly inside regulated environments without increasing risk. The relevant consideration is not the inevitability of AI use in medical writing, but rather the selection of AI approaches that demonstrably meet the standards of trust and reliability required in regulated medical documentation.

[1]. Fattorini F. Can AI replace medical writers? Experts say not immediately. Clinical Trials Arena. April 11, 2025. https://www.clinicaltrialsarena.com/features/can-ai-replace-medical-writers-experts-say-not-immediately

[2]. Markey N, El-Mansouri I, Rensonnet G, van Langen C, Meier C. From RAGs to riches: Utilizing large language models to write documents for clinical trials. Clinical Trials. 2025 Feb 27. https://pmc.ncbi.nlm.nih.gov/articles/PMC12476469/

[3]. Waldock WJ, Zhang J, Guni A, Nabeel A, Darzi A, Ashrafian H. The Accuracy and Capability of Artificial Intelligence Solutions in Health Care Examinations and Certificates: Systematic Review and Meta-Analysis. J Med Internet Res. 2024 Nov 5. https://pmc.ncbi.nlm.nih.gov/articles/PMC11576595/