Benchmarking real-world accuracy, speed and compliance when large-language-models go head-to-head with human expertise.

The Stakes: Why the Debate Matters for Clinical-Trial Documentation

Clinical research lives and dies by its paperwork. From the first synopsis of a protocol through to patient-facing leaflets, every sentence must survive regulators, ethics committees and, ultimately, public scrutiny. The volume is immense—sponsors can generate more than 10,000 pages for a single Phase III study—and the cost of delay is counted in lost patent time and, more importantly, postponed therapies.

Enter generative AI. Large-language-models (LLMs) such as GPT-4 can draft fluent biomedical prose in seconds, ingesting style guides and journal conventions that take junior writers years to master. For contract research organisations and sponsors alike the attraction is obvious: shorten document timelines, redeploy writers to high-value tasks and, in theory, reduce human error. Yet seasoned writers counter that apparent polish can mask subtle logical faults or guideline mismatches. Strip away the hype and you are left with three core questions:

• Accuracy – does machine text stand up to expert fact-checking?
• Speed – do time savings in drafting survive the inevitable review cycles?
• Compliance risk – who is accountable when an algorithm hallucinates?

Recent empirical studies allow us to move the conversation from anecdote to evidence—revealing a rather more nuanced answer than the “robots will replace us” rhetoric suggests.

Accuracy: High Surface Fluency, Uneven Depth

Several head-to-head evaluations have put GPT-4 through its paces against experienced medical authors. In qualitative health-care research, GPT-4 agreed with human coders on major interview themes but diverged on fine-grained sub-themes, yielding only moderate agreement (Cohen’s κ ≈ 0.40) [3]. A separate randomised assessment in oncology patient-education materials painted a brighter picture: reviewers judged 87 % of GPT-4 pamphlets to be fully aligned with national guidelines, and readability scores matched—or exceeded—those produced by hospital education teams [4].

Why the difference? LLMs excel at lexical correctness—terminology, grammar and overall coherence—but remain brittle where deep reasoning and cross-document logic are required. A 2024 study that asked GPT-4 to draft scientific review articles found that 70 % of references in AI-only drafts were inaccurate or entirely fabricated [5]. This failure mode, commonly called hallucination, is especially dangerous in regulated settings where traceability of evidence is a legal requirement.

Regulators are taking note. The European Medicines Agency’s 2024 Reflection Paper on Artificial Intelligence reminds applicants that sponsors “remain fully accountable for the accuracy and traceability of text produced by machine-learning systems,” and stresses the need for immutable audit trails [6]. In other words, an LLM can shoulder the typing but not the liability.

Productivity Gains: Where Machines Really Shine

If accuracy is a mixed bag, speed is not. An MIT-led experiment involving 453 college-educated professionals found that access to ChatGPT cut median drafting time by 40 % while independent graders reported an 18 % rise in quality [1, 2]. The biggest beneficiaries were lower-performing writers, suggesting LLMs act as a leveller of basic craftsmanship.

Clinical-trial documentation shows a similar pattern. Internal sponsor pilots (data on file) indicate that once key study parameters are captured in a structured form, an LLM-enabled workflow can assemble full protocol shells, informed-consent forms and investigator brochures in under an hour—tasks that traditionally devour several weeks. Even if subsequent review doubles the initial drafting time, the overall cycle shrinks dramatically.

There is, however, a hidden cost: prompt engineering and validation. Teams must invest in building robust style prompts, domain-restricted knowledge bases and automated consistency checks. Without that scaffolding, reviewers may spend longer hunting for subtle AI errors than they would have spent drafting from scratch.

Governance and the Hybrid Model: Humans in the Loop

The EMA paper frames AI adoption through the lens of risk management: data privacy, model drift and regulatory mismatch are chief concerns [6]. Three practical safeguards have emerged as industry best practice:

1. Data segregation and retrieval-augmented generation (RAG). Feeding patient-level data into a public model is a non-starter under GDPR. RAG architectures pull only sanctioned snippets from a secure knowledge store, ensuring provenance and version control.
2. Immutable audit trails. Modern authoring platforms record every prompt, response and manual edit, creating a chain of custody that satisfies inspectors.
3. Role re-definition. Medical writers transition from “primary authors” to editors-in-chief—curating prompts, validating outputs and, critically, applying domain judgement where LLMs still falter.

Early adopters report that such hybrid teams deliver faster and safer documents. Writers spend less time re-keying boilerplate and more time shaping scientific narratives, while project managers gain real-time visibility into a single source of truth. The result is not man versus machine but man plus machine, each compensating for the other’s weaknesses.

So, will generative AI outperform your best medical writer? On the metric of raw drafting speed, it already does. On deep scientific reasoning and regulatory nuance, it does not—yet. The optimal path forward is partnership: let algorithms churn out the first 80 %, then deploy human expertise to elevate the final 20 % from merely competent to submission-ready. Teams that master this choreography stand to cut timelines, contain costs and, ultimately, bring therapies to patients sooner—all without compromising the rigour on which clinical research depends.

References

1. Noy S, Zhang W. Experimental evidence on the productivity effects of generative artificial intelligence. Science. 2023;381(6658): 187-192.
2. Winn Z. Study finds ChatGPT boosts worker productivity for some writing tasks. MIT News. 14 July 2023.
3. Li KD, Fernandez AM, Schwartz R, et al. Comparing GPT-4 and human researchers in health care data analysis: qualitative description study. J Med Internet Res. 2024;26:e56500.
4. Rodler S, Cei F, Ganjavi C, et al. GPT-4 generates accurate and readable patient education materials aligned with current oncological guidelines: a randomised assessment. PLoS One. 2025;20(6):e0324175.
5. Kacena MA, Plotkin LI, Fehrenbacher JC. The use of artificial intelligence in writing scientific review articles. Curr Osteoporos Rep. 2024;22(1):115-121.
6. European Medicines Agency. Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle. EMA/CHMP/CVMP/83833/2023. Final version adopted 9 September 2024.