AI-Assisted Narrative Drafting — Where It Helps and Where It Doesn’t

Generative AI drafting tools have become a normal part of narrative writing workflows, and it’s worth being precise about exactly what they’re good at. Given the structured grounding data for a case — demographics, dates, drug and event details, treatment, outcome — a model can assemble a coherent first-pass narrative draft, correctly sequenced, in the standard six-part structure, in a fraction of the time a manual first draft takes. That’s a genuine, substantial time saving, and it’s the reason these tools have spread quickly across safety databases.

What the tools are not reliable for is the causality statement, or any clinical judgment about what the case actually means. A model drafting from grounding data will produce prose that sounds confident and clinically fluent regardless of whether its underlying reasoning about causality is sound — which is precisely the danger. A causality conclusion that reads well is not the same as a causality conclusion that’s correct, and a trained writer reviewing an AI draft has to independently verify the stated causality against the actual case facts, not just check that the sentence is grammatically and stylistically fine.

The objectivity discipline from Lesson 5.3 needs to be applied to AI-drafted narratives even more deliberately than to human-written ones, somewhat counterintuitively. Generative models trained on large volumes of medical and general text tend to produce more confident, more editorialised-sounding language by default than a trained PV writer would — phrases that lean toward "clearly" and "obviously" more often than a human writer following house style would use. A reviewer working through an AI draft should specifically be checking for exactly this pattern, not assuming a machine-generated draft is automatically more neutral than a human one.

None of this is a reason to avoid the tools — it’s a reason to use them correctly. The same human-in-the-loop principle covered for MedDRA coding in Module 4 and for causality assessment in Module 6 applies here without modification: a qualified human reviews, edits, and signs off on every narrative before it’s finalised, regardless of whether the first draft came from a person typing from scratch or a model generating from structured data. The efficiency gain is real; the accountability for what the finished narrative says never transfers to the tool that helped draft it.