The AI they rolled out isn’t the AI you need
A colleague’s IT team “rolled out AI to everyone” last week — meaning the assistant in the browser, the one that summarises your tabs and rewrites your emails. The medical writers were to use it like everyone else. Reasonable, free, already there, and a quiet category error. The general-purpose assistant is built to be passable at everything for everyone; our work is narrow, exact and unforgiving of the near-enough. Harvard Business Review made the wider point this week: healthcare’s specialised dialects sit beyond what a generic model handles dependably. The danger was never that the tool is bad — it’s that it’s good enough to be trusted by people who can’t see where it fails, and in our field the failures stay invisible until someone qualified looks. When the rollout email lands, the useful reply is a question: trained on what, grounded in which sources, checked by whom?
Source link → Harvard Business Review: https://hbr.org/2026/03/healthcare-uses-specialized-language-it-needs-specialized-ai-too
The unfashionable question, asked out loud
For two years the conference AI sessions ran to a script: a demo, some adoption statistics, a closing slide about the future arriving whether you like it or not. That’s changing. STAT reported on panels where scientists are finally asking the unfashionable question aloud — not “is it impressive?” but “does it actually work?” We sit downstream of these tools; when a client’s research team runs a literature scan through an AI platform, the output flows into our copy and our name goes on the claim at the end. So practitioner scepticism isn’t something to borrow sheepishly. It’s cover. Make the question respectable in your own rooms: validated against what, on whose data, and who notices when it’s wrong?
Source link → STAT: https://www.statnews.com/2026/05/21/are-ai-scientist-tools-actually-useful-ai-prognosis/
When the builder is more cautious than the brief
The brief wanted copy on how the company “used AI to design” its lead molecule — the implication, unmistakably, speed. The trouble is that the person best placed to know was more careful than the marketing. In an interview with STAT, the chief executive of an AI-driven biotech drew the line: AI accelerated the early design — the candidates worth making — while everything after, the assays, safety, trials and regulators that decide whether a drug exists, runs at the old speed. The clever bit is narrow; the slow bit is most of it. When the person who built the model won’t overclaim, the writer downstream certainly shouldn’t. My test now: would the scientist who did the work nod, or wince? Write for the nod.
Source link → STAT: https://www.statnews.com/2026/05/26/ai-biotech-bighat-biosciences-ceo-on-ai-drug-development-hype/
— Ned
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