AI: Solving a Problem That Doesn’t Exist

The last 3 years have witnessed a rapid surge in the adoption of for artificial intelligence (AI) and large language models (LLMs) across the pharmaceutical industry. Tools capable of producing fluent, seemingly structured scientific text have prompted claims that the traditional writing of Clinical Study Reports (CSRs) can be compressed, automated, or even replaced entirely. For some, the promise is irresistible: LLMs can ‘write’ a CSR draft in minutes, offering the possibility of shaving weeks (some hope months) from development timelines [1]. The implication is that medical writers, trained professionals who have spent decades crafting regulatory-ready documents, represent a bottleneck ripe for elimination.

But this narrative is misleading. It reflects a fundamental misunderstanding of both the purpose of the CSR and the true sources of delay in drug development. In reality, AI is being promoted as the solution to a problem that never existed. The writing of CSRs in itself is not the limiting factor in programme timelines if you use an experienced medical writer [2]. Nor is it merely an administrative exercise of stitching together tables and findings. Rather, it is a complex, interpretive, and regulatory-critical task that depends on human judgment, cross-functional reasoning, and a depth of scientific understanding that current AI systems cannot replicate. The claims that AI can, or should, replace professional medical writers is not merely inaccurate, it risks undermining the quality and reliability of evidence submitted to regulators.

An Intellectual, Not Administrative Task

The CSR has long been misunderstood by those not involved intimately in research. Far from being a uniform compilation of outputs, it is the authoritative scientific account of a clinical trial that forms a key component of any regulatory submission. Its required content, structure, and intent are defined by ICH E3 [3], but the document is far more than a template. It demands a coherent interpretation of the trial’s design, conduct, analysis, and outcomes, framed in a way that is clinically meaningful and scientifically defensible. A well-written CSR is a narrative synthesis, one that explains not only what happened in the trial but why it happened, what it means, and how it should be contextualised within the broader development programme. Anyone who claims otherwise is selling snake oil.

Professional medical writers contribute to this synthesis by integrating clinical, statistical, pharmacokinetic/pharmacodynamic, and safety information in collaboration with cross-functional experts. Regulators rely on this clarity. Studies show that well-prepared regulatory documents reduce agency queries, facilitate smoother reviews, and prevent unnecessary rework [3]. Conversely, poorly crafted CSRs, whether produced in haste, assembled without adequate oversight, or generated by systems that lack comprehension, introduce inconsistencies and errors that delay and confound submissions rather than accelerate them.

The Myth of the Writing Bottleneck

Despite the centrality of CSRs in regulatory submissions, writing them is not what slows development. Industry surveys, operational audits, and analyses from the Tufts Center for the Study of Drug Development repeatedly identify the same culprits behind delays: data cleaning, database lock, post-hoc analyses, governance reviews, and cross-functional alignment [4][5][6][7]. The other reason for slow delivery relates to poor vendor selection, bulking the CSR writing into trial budgets without interrogating your CRO’s prioritises capacities and capabilities. These elements account for the majority of the time between last-patient-last-visit and submission, leaving writing as only one small element of a much longer and more scientifically complex process.

Indeed, database lock is routinely delayed by errors in derived datasets, incomplete listings, or under-resourced clinical data management. Even after data are released, teams often require extensive discussion to interpret ambiguous findings or resolve discrepancies across safety, efficacy, or pharmacokinetic results [6]. The Niche writers are not strangers to raising issues about allegedly ‘final’ data. These interpretive activities, requiring clinical reasoning, statistical debate, and programme-level judgment, cannot be accelerated simply by automating text generation. The delay exists upstream of writing, not within it.

The belief that CSRs take too long has therefore been shaped more by frustration with cross-functional processes than by evidence. Producing a first draft within hours or minutes does not accelerate the steps that occur before writing begins, nor does it shorten the team review cycles that follow. AI-facilitated disengagement from report development leads to reduced contemplation times, pressured team members, and poorly informed team meetings. Consequently, automating CSR production undermines the review process and is likely to introduce new errors that subsequently generate additional delays.

AI and LLMs: Capable Partners, Not Independent Authors

There is no doubt that modern LLMs exhibit impressive capabilities. When provided with structured content (data tables and figures), clearly defined inputs, and tightly constrained tasks, AI can summarise results, produce descriptive text, and help enforce stylistic consistency. Output may, at first sight, look almost as good as a Niche writer’s CSR [8]. Early studies indicate that LLMs can generate coherent summaries of clinical data in controlled settings and several CROs are offering these tools to clients [9]. Larger sponsor companies have their own in-house systems. There is no doubt that these capabilities are potentially time valuable and offer opportunities for workflow efficiency.

Yet LLMs bring intrinsic limitations. By design, they are probabilistic systems that generate text based on statistical likelihood, not understanding. They lack awareness of biological plausibility, clinical relevance, regulatory context, and causal reasoning that may occur during review meetings. Evaluations of LLM-generated clinical outputs reveal hallucination rates ranging from 6 to 22%, even when training is restricted to structured data [10]. For a regulatory document, such variability is unacceptable [11].

Moreover, LLMs cannot adjudicate independently the interpretive decisions that define a CSR. They cannot distinguish whether an imbalance in adverse events represents a clinically meaningful signal, whether an unexpected pharmacokinetic profile warrants caution (and alternate reanalysis), or whether a protocol deviation meaningfully affects the robustness of study findings. These judgments must be made by humans [11].

The reproducibility issue adds a further complication. Regulatory submissions require a clear chain of reasoning from data to conclusions. Yet LLM outputs vary with different prompts, updates to the underlying model (any new data added to the model), or even random sampling parameters. The MHRA notes that such variability from ‘black box’ technology undermines evidentiary standards unless systems are validated rigorously and used within tightly controlled boundaries [12]. The GAMP® AI guidance likewise emphasises that sponsors retain responsibility for all generated content, irrespective of the technology employed [13].

Both the EMA and FDA have recently issued reflections on the role of AI in regulatory submissions. While they currently recognise the utility of AI as an assistive tool, they emphasise that automated systems should not replace scientific interpretation or sponsor accountability. The EMA’s 2023 reflection paper explicitly states that final text must reflect “the applicant’s own scientific understanding,” not that of an automated generator [11]. The FDA has reiterated that Sponsors must ensure accuracy, traceability, and reproducibility in all submitted content, irrespective of how drafting may be conducted [14]. Any QC of a CSR will still need to be signed off by an appropriately qualified human.

In other words, regulators expect human medical writers, and their cross-functional collaborators, to remain responsible for the conceptual integrity and scientific coherence of CSRs. This may change but one might hope only with a thorough understanding of the disciplines practiced and contributions made by medical writers.

Why Medical Writers?

To suggest that medical writers can be replaced by automated systems is to fundamentally misjudge their role. Far from merely compiling text, they serve as stewards of scientific integrity within the clinical development programme. Writers ensure that analyses are accurately represented, that all inconsistencies are resolved or explained, and that interpretive claims remain within the bounds of evidence.

Their value lies in their capacity for clinical reasoning, pattern recognition, and narrative coherence, skills that are deeply contextual, experience-based, and inherently human. Medical writers understand the regulatory expectations that govern terminology, style, and structure. They recognise the nuances that distinguish an emerging safety signal from noise. If nothing else, a good medical writer is well versed in conflict resolution and consensus building, let’s see an AI do that. When integrated in the CSR review team experienced medical writers will identify when a finding contradicts earlier programme conclusions, and they ensure that the CSR narrative integrates these complexities transparently. AI cannot yet replicate these capacities, because they depend on judgment rather than language generation. By staying on top of review cycles (and reviewers) they can also drive projects to a prompt completion.

Writers also play a crucial role in institutional memory. Through repeated exposure to health authority interactions, therapeutic area strategies, and product-specific challenges, they contribute historical and contextual insight that AI tools, even when fine-tuned, cannot provide.

Augmentation Not Replacement

The pharmaceutical industry has long attempted to compress development timelines. But many well-intentioned initiatives have failed specifically because they targeted symptoms rather than causes. The push to automate CSR writing risks repeating this pattern. Medical writing is a moot case. The real reason why writing CSRs drag on relates to senior team members not reviewing Draft 1 (because they are too important to get into the weeds), indecision and failure to close issues, re-evaluation of findings and your CRO not prioritising your CSR delivery.

A far more promising future strategy embraces hybrid workflows in which AI assists but does not replace medical writers. Early evidence shows that AI can support tasks such as summarising patient disposition, generating baseline text, identifying inconsistencies, and applying template rules [15]. In such configurations, AI acts as a labour-saving tool that helps writers redirect their time toward higher-value interpretive tasks. But don’t expect to finalise your CSR months earlier than by traditional methodologies.

This may be a more realistic vision of the future of CSR writing: a partnership between human insight and computational efficiency, not the substitution of one for the other. There remains the argument that having a medical writer building a report from the ground up is that they bring a deep understanding of the study design and conduct to CSR review meetings. AI has much to offer, but only when deployed as a complementary tool. Regulatory documents demand careful interpretation, scientific reasoning, and narrative coherence, requirements that remain beyond the independent capabilities of AI (as yet). Medical writers continue to provide the judgment, insight, and integrative skill that make CSRs scientifically and regulatorily robust.

Conclusion

Let me say this clearly for all the AI-fawning sycophants, this Emperor has no clothes! Yes, the excitement surrounding AI in clinical writing stems from genuine technological progress. However, believing that AI can replace medical writers misunderstands the nature of regulatory writing itself. They certainly demonstrate limitations in clinical thinking, logic, and transparency among other factors [16][17].

The writing task has never been the true rate-limiting factor in drug development, and efforts to automate it will not meaningfully accelerate submissions. Instead, they risk creating new quality, consistency, and regulatory vulnerabilities. You might want to ask your CRO why they desperately want you to use their tool, could it relate to their lack of commitment to your project.

References

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