A note from our CEO & Founder: Acumen’s AI Development

Acumen’s AI Development  

In the world of scientific and medical writing, data integrity and accuracy are everything. A medical writer’s role is to curate, present, organize, and interpret complex data. Ensuring that every data point is 100% true to source is non-negotiable.  

This is where many current generative AI systems fall short. Tools like ChatGPT can generate fluent content, but they are prone to errors and “hallucinations”—problems that make drug developers understandably cautious, if not outright resistant, to their use in regulated environments.  

Over the past year, we’ve seen a surge of AI-powered platforms promising to accelerate document generation for both regulatory agencies and scientific audiences alike. On the surface, this seems appealing: if a writer can polish a draft with AI assistance, what’s the harm? In fact, for tasks such as grammar corrections, style alignment, or condensing existing content, large language models (LLMs) can provide clear benefits.  

But content creation and interpretation are a different story. Anyone who has worked with AI systems—from ChatGPT to Copilot to emerging industry tools—knows the warnings well: these systems can introduce mistakes. And in medical writing, even minor errors can have major consequences.  

That’s why at Acumen, our principle is simple: content is written and interpreted by human experts first. When we use AI, it is for supportive tasks—polishing drafts, rephrasing text, or summarizing material—never for introducing or interpreting clinical data.  

At the same time, we recognize the potential for AI to transform how clinical developers work. The vision is clear: machine-assisted tools that help writers, developers, and medical teams handle higher-order tasks with confidence, without sacrificing accuracy.  

Yet, this vision cannot be achieved overnight. It requires careful design, testing, and validation—step by step. It requires a step-by-step process of careful design. At Acumen, we are combining LLMs with other forms of machine learning to develop targeted, error-resistant tools that empower clinical developers without compromising data fidelity.  

Our approach is deliberate, not rushed: test, refine, and confirm accuracy before deployment. Later this fall, we look forward to sharing the first glimpse of Acumen’s AI tools—built not on hype, but on a foundation of trust, precision, and real-world utility.  

More to come …  

Regards,  

Justin McLaughlin  

CEO and Founder