AI picks up more of the load-bearing parts of our work — faster than it is becoming reliable.
The MHRA starts drawing the rules for AI that won’t sit still Dame Jennifer Dixon, writing for the MHRA’s strategy series, sets out the genuinely hard part of regulating AI that adapts in the real world rather than staying a fixed “product”: not just whether a model is safe and accurate at launch, but whether it stays so as it drifts, and whether it is usable, acceptable and fair in practice. Her sharper point is one health communications should sit with — NHS local evaluations are often weak, with strong incentives to over-claim results. That temptation will land on anyone writing up an AI tool’s performance, and the credibility bar is about to rise.
Digital twins move from simulating the trial to steering it
In a sponsored interview — read it as a vendor’s pitch — Unlearn describes pushing digital-twin models out of trial simulation and into the whole decision chain: planning, blinded data monitoring, analysis. The substantive signal under the marketing is real, though: its PROCOVA prognostic-covariate-adjustment method now carries EMA qualification and aligns with FDA guidance, and Phase 3 trials using digital-twin analysis are described as imminent. For anyone communicating trial design or evidence, the synthetic-comparator conversation has stopped being hypothetical — and “take the regulators seriously, early and often” is the line worth stealing.
Does AI spell the end for HEOR?
The Healthcare Economist poses the question every value-and-evidence team is quietly asking: if a model can draft the literature review, build the economic model and assemble the dossier, what is left that is irreducibly human? The answer worth holding is that payers buy credibility, not output — and a model fluent enough to generate a cost-effectiveness case is equally fluent at generating a plausible wrong one. The work that survives is the judgement that defends the assumptions, not the keystrokes that produced them.
TechCrunch charts the scramble now that the metered cost of running large-language-model workflows at scale has stopped being a rounding error. For agencies and medical teams that have quietly routed literature reviews, drafting and slide production through these tools, the lesson is unglamorous but overdue: budget for usage as you would any other input, because the per-token economics now scale with your volume, not your headcount. The free-tier era of “just try it on everything” is closing.
Reword the question, change the diagnosis
A new study finds clinical LLMs remain acutely sensitive to small prompt changes — the same question, phrased two ways, can yield different answers. For patient-facing or clinical content, that fragility is the whole risk: reliability that depends on exact wording is not reliability at all. It is the standing argument for keeping a human between the model and anything a patient or regulator will read, made once more, with data.
That’s it for this edition. Back Wednesday.
— Ned
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