How is the rise of GenAI changing the field of pharma medical writing?

Pharma medical writing is an onerous, ongoing activity that could benefit hugely from tech-enabled disruption, and the latest advances in generative AI (GenAI) show promise in alleviating the pain.

But as companies have begun to develop GenAI capabilities internally, a realisation has dawned that they need a unique combination of technical aptitude and regulatory domain (hands-on) experience when it comes to the complex path to reliable, efficient automation.

An intervention requiring human oversight

Biopharma medical writing is an ideal target for GenAI-based process transformation, with its large volumes of highly regulated, templated repeat activities, for example, around license application preparation and safety report generation. Even if GenAI could complete a strong first draft of these intricate documents, with oversight from skilled humans, that intervention alone would do much to relieve the strain on busy regulatory professionals and neighbouring colleagues.

Yet, even with the benefit of large language models (LLMs), to build system ‘knowledge’ GenAI technology needs a lot of moulding to be of trusted value. Even with advanced natural language processing (NLP) and deep learning capabilities, AI models need to be guided in how to repurpose information and data correctly, and what ‘good’ looks like.

To yield tangible improvements to efficiency, AI skills need to be combined with life science industry fluency in specialist language and vocabularies, required templates, and the nuanced demands of each market. That includes in-depth knowledge of what will be accepted by regulatory agencies, and how that differs from region to region, and country to country, as well as a strong feel for specific medical writing best practices linked to each use case.

It takes vast reams of successful example content to ‘teach’ a GenAI model what’s needed, meanwhile - and the ideal output to aim for. For pharma companies to bring their own capabilities up to speed, and stay ahead, while also maintaining regulatory compliance seems a challenging feat.

Pharma’s capability gap

A survey conducted recently by Celegence with the Regulatory Affairs Professionals Society (RAPS) highlighted medical writing as a critical area requiring support, due to growing pressure on Regulatory professionals’ time. Over half (57%) of surveyed companies were planning to invest in technology to improve medical writing over the year ahead, almost on a par with eCTD v4.0 spending, the two categories dominating immediate Regulatory IT spending plans.

Clinical study protocol/report writing and drafting of regulatory documents were the primary medical writing needs identified by Regulatory professionals, requiring additional support. More than half of respondents in the 2024 study identified a need to harness AI in data extraction (56%) and information summarisation (53%), where just 9%- 10% are using AI for those purposes today - specifically within the context of medical writing. Meanwhile, 12% said they were actively in the process of incorporating AI into automated report generation from multiple sources, which is the ultimate opportunity on offer.

Despite these ambitions, more than half (53%) of survey respondents in pharma conceded that their organisations did not possess sufficient knowledge internally to implement AI technologies themselves.

Demonstrating technology integrity

Other potential barriers to AI uptake revolve around confidentiality and the scope for inadvertent breaches, linked in part to how the technology is ‘prompted’ to call up information. Because the technology is so complex below surface level, there are concerns that applying public cloud-based GenAI models could compromise internal data security.

All of these concerns are readily addressable in an appropriate ‘closed’ processing environment that has been purposefully tailored to life sciences use cases. This ensures that all data interactions remain within a secure and controlled ‘space’, for data confidentiality and integrity purposes.

Other considerations include traceability and auditability: the ability to see where extracted data has come from or from which source content summarisation has been achieved (for instance, clear links in the final report to original documents). This is essential to build confidence and trust in solutions, so that they are seen to add significant value and save time; over-reliance on painstaking checks could undermine the return on investment.

Knowing that there are solutions that can be validated for priority pharma medical writing use cases will be an important facilitator for companies with a growing need for smarter support, as medical writing workloads continue to soar, rather than diminish, over time.

Beyond process efficiency: Leaner & cleaner output

In looking for the value from an effective (and, ideally, fully managed) GenAI medical writing automation capability, pharma companies need to look not just at the efficiency gains associated with smart data extraction, information summarisation, and narrative authoring – albeit, these are promising areas to start focusing on. Rather, further gains will come from improvements to consistency, and to leaner, tighter output once specified regulatory documents are being drafted according to training (from extensive exposure to approved documents) on what ‘good’ looks like.

Investing in R&D or partnership with external technology solution providers on a variety of use cases promises to make better use of scientific experts’ time, as use of their time shifts from initial drafting to the strategic thinking in collaboration with clinical development professionals.

In a more strategic context, in early clinical evaluation, Gen-AI based tools could help summarise the vast wealth of existing information on the internet to hone the focus of planned research in order to avoid wasted time investment. AI-assisted search across multiple sources can also summarise headline findings, while highlighting what emerges as the best path to follow, too.

Keeping pace with an advancing market

Although biopharma companies themselves may lack sufficient R&D resources internally to play around with possibilities, experimentation is a powerful way forwards in determining where GenAI offers maximum value in transforming medical writing. Partnering with an organisation that has both hands-on experience with GenAI models and writing lean, first-time approved regulatory documentation is advisable. The risk of inertia is of falling behind, as innovators continue to break new ground.