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AI & Healthcare

226 summarised stories about AI & Healthcare, each linking back to the original source. Browse all topics →

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Thursday, 10 September 2026

Aflac President: why AI shouldn't drive your healthcare

Fortune 31

Virgil Miller, president of Aflac, argues that people should use AI to support healthcare decisions but not replace physician care or in-person screening. The article cites a study that found 76% of Gen Z and 63% of millennials use AI as their first source for primary healthcare before seeing a doctor. It shifts the message from getting medical information from AI toward taking action with doctors, screenings, and trusted encouragement to close the gap in delayed care.

Belgium spin-out IVEX raises €5M to cut car safety testing time, eyes US and Asia

Tech Funding News 1 month ago 6

IVEX raised a €5M Series A to bring its car safety-testing AI platform to the US and Asia. The platform cuts a single batch footage review from 400 hours to eight hours, with engineers supervising each result. As a result, IVEX plans to expand coverage of NCAP protocols and build regulatory and commercial presence in North America and Asia.

Genome language models can design cancer vaccines

TLDR 1 month ago 38

Genome language models were used to support a cancer-vaccine design workflow covering mutation identification, antigen presentation and immunogenicity prediction, target ranking, and RNA design. The approach is presented as a test of whether the models can design a vaccine for one human being. As a result, cancer-vaccine development is framed around evaluating AI’s ability to apply biology knowledge to personalized medicine.

UK needs new laws for AI in healthcare, says watchdog

BBC News 1 month ago 29 ● 125 sources

Britain’s medicines and medical device watchdog, the MHRA, published 44 recommendations for how the UK should regulate AI products used in NHS and other healthcare settings. The report was based on input from more than 12,000 people. The proposals would add ongoing monitoring with possible withdrawal if performance slips, require clearer disclosure to patients, allow penalties for developers, and introduce supervised trials for new AI models via an “L plate” approach.

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