This edition’s throughline: the scaffolding is going up around AI in our field — regulators converging on one rulebook, the hyperscalers wiring into pharma’s basement, a state-funded testbed, the search layer rerouting the audience — while the thing being scaffolded still fails the plain reliability test. Worth holding both at once.
The regulators have started writing one rulebook, not five
The EMA and FDA put out ten joint Principles for Good AI Practice in January, and this month the EMA and HMA published their 2025 AI observatory report to steer the 2026–2028 workplan. The substance for health communications isn’t the principles themselves; it’s that the two largest regulators are converging on a single vocabulary for how AI-touched evidence must be described and monitored. When you write up anything a model helped generate, the standard you’ll be held to is being standardised — better read now than met for the first time in a query letter.
The hyperscalers are moving into the basement
Lilly and NVIDIA announced a co-innovation lab to rebuild drug discovery around AI; TCS and Anthropic signed a global partnership the same fortnight. Read it as a forecast of your brief queue. The “AI-accelerated discovery” story is about to be load-bearing in a lot of pipeline communications, and the gap between what the model actually did and what the headline implies is exactly where a careful writer earns their keep. Compressing the first mile is real; it is not the same as finishing the race faster.
Patient-facing accuracy still isn’t there for the messy diseases
https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1847603/full
A June study benchmarked leading models on patient questions about heart failure and cardiomyopathy and found their answers not yet dependable for clinically appropriate, comprehensible guidance on heterogeneous conditions. This is the unglamorous counter to every “just put a chatbot on it” proposal: the harder a disease is to generalise, the worse the model does — and those are precisely the conditions patients search most anxiously. Keep a human between the model and anything a patient reads.
The audience is being rerouted before it reaches your page
https://upgrowth.in/google-ai-overviews-healthcare-traffic-data/
AI Overviews now appear on roughly half of health searches, with click-through down around 60 per cent and some medical publishers reporting 70 per cent traffic drops on the pages an overview answers directly. For any client who owns clinical content, organic reach is being quietly restructured: the prize is no longer ranking first, it’s being the source the AI answer cites. If your content strategy still assumes the click, it is planning for a web that is receding.
The UK built a place to test the things before they ship
The MHRA has put a further £3.6 million over three years into its AI Airlock, the supervised sandbox for AI as a medical device. It won’t touch most promotional work directly, but it’s worth knowing for two reasons: it’s where the evidence that shapes the next round of guidance is being generated, and it gives you a concrete, government-run answer when a client asks “where has this actually been tested?” — instead of pointing at the vendor’s own deck.
That’s it for this edition. Back Wednesday.
— Ned
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