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AI can forecast you, just like the weather

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Hello readers,

Welcome to the AI For All newsletter! Today, we’ll be exploring the future of AI-powered preventative healthcare, the role of AI in modern visual inspection, and more!

AI in Action: Delphi-2M

A new AI model called Delphi-2M is reshaping what’s possible in preventive healthcare, up to and including forecasting your health like the weather, reports the BBC. Developed through a collaboration between the European Molecular Biology Laboratory, the German Cancer Research Centre (DKFZ), and the University of Copenhagen, Delphi-2M was trained on over 400,000 anonymized UK Biobank records and later validated on 1.9 million Danish patients. That gives it the power to calculate the death risk of 1,200 health conditions simultaneously, using patterns in medical records the same way ChatGPT predicts words in a sentence.

"So, just like weather, where we could have a 70% chance of rain, we can do that for healthcare," Prof Ewan Birney, the interim executive director of the European Molecular Biology Laboratory, told the BBC.

The model excels at identifying predictable disease trajectories such as type 2 diabetes, heart attacks, and sepsis, offering estimates up to a decade in advance. That makes it a potential game-changer for both patients and healthcare systems. By flagging high-risk individuals earlier, Delphi-2M could drive timely interventions like targeted lifestyle guidance or early medications and help hospitals prepare for future demand. Imagine knowing how many strokes, heart attacks, or sepsis cases a city might face in 2030, and being able to staff and supply accordingly.

While the tool isn’t ready for clinical use just yet, its promise is clear. Researchers envision it supporting personalized prevention strategies, optimizing screening programs, and even helping allocate resources at the regional level.

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📖 What We’re Reading

The Role of AI in Modern Visual Inspection

“In manufacturing, AI-powered visual inspection is rapidly emerging as a game-changer, either replacing or complementing traditional manual checks and rigid, rule-based systems. But why is this approach so effective? How does it stack up against the old ways of doing things? And when we talk about “AI” in the context of visual inspection, what does that actually mean?”