"The technology has learned to spot patterns in people's medical records to calculate their risk of more than 1,000 diseases.
The researchers say it is like a weather forecast that anticipates a 70% chance of rain – but for human health.
Their vision is to use the AI model to spot high-risk patients to prevent disease and to help hospitals understand demand in their area, years ahead of time...
The AI model was initially developed using anonymous UK data - including hospital admissions, GP records and lifestyle habits such as smoking - collected from more than 400,000 people as part of the UK Biobank research project[1]."
I'm curiously interested in understanding long-term health risk, and how to lower these, potentially with early interventions.
We’re at the dawn of primary prevention. Not only are there many new layers of data—organ clocks, biomarkers, genomics, biosensors—but we have multimodal A.I. and agentIc A.I. to analyze the data. For the first time, we are seeing a large health model (Delphi-2M) that has learned the grammar and language of health, tokenizing it to predict diseases with temporal anchoring. We’ve gotten used to large language models that predict the next word in a sentence, but just imagine how powerful a large health model (LHM) will be when all the other layers of data beyond those utilized in Delphi-2M are integrated. Yes, 0.76 AUC performance for prediction across all diseases isn’t great, but this is just the beginning. From Delphi-2M, we learned that person’s health story can be projected 20 years ahead. This represents a jump from my prior piece on precision medical forecasting. Future models will keep improving on precise medical forecasting.
At the same time, we’re seeing the most advanced personal health agent that has yet been developed and validated, which can incorporate layers of data not previously utilized, with interactivity and long-term memory that may help promote healthy lifestyles for users. No, it won’t work for everyone, but given poor adoption of healthy lifestyle factors (for example, 75% of Americans do not even fulfill the minimal recommendations for physical activity), it would be hard to think it won’t help to some extent.
There’s currently a lot of negativism about generative A.I. I get it. But the new era of primary prevention would not be possible without it. It will take time to get this to be standard of care, that is real prevention, but I believe it will ultimately wind up being seen as the most important contribution of A.I. for promoting human health.
aanet•4mo ago
This looks intriguing. From the article:
"The technology has learned to spot patterns in people's medical records to calculate their risk of more than 1,000 diseases.
The researchers say it is like a weather forecast that anticipates a 70% chance of rain – but for human health.
Their vision is to use the AI model to spot high-risk patients to prevent disease and to help hospitals understand demand in their area, years ahead of time...
The AI model was initially developed using anonymous UK data - including hospital admissions, GP records and lifestyle habits such as smoking - collected from more than 400,000 people as part of the UK Biobank research project[1]."
I'm curiously interested in understanding long-term health risk, and how to lower these, potentially with early interventions.
FWIW, the Nature paper quoted is here [2]
[1] https://www.ukbiobank.ac.uk/
[2] https://www.nature.com/articles/s41586-025-09529-3
aanet•4mo ago
https://erictopol.substack.com/p/dawn-of-a-new-era-of-primar...
Quoting his summary here:
<QUOTE>
We’re at the dawn of primary prevention. Not only are there many new layers of data—organ clocks, biomarkers, genomics, biosensors—but we have multimodal A.I. and agentIc A.I. to analyze the data. For the first time, we are seeing a large health model (Delphi-2M) that has learned the grammar and language of health, tokenizing it to predict diseases with temporal anchoring. We’ve gotten used to large language models that predict the next word in a sentence, but just imagine how powerful a large health model (LHM) will be when all the other layers of data beyond those utilized in Delphi-2M are integrated. Yes, 0.76 AUC performance for prediction across all diseases isn’t great, but this is just the beginning. From Delphi-2M, we learned that person’s health story can be projected 20 years ahead. This represents a jump from my prior piece on precision medical forecasting. Future models will keep improving on precise medical forecasting.
At the same time, we’re seeing the most advanced personal health agent that has yet been developed and validated, which can incorporate layers of data not previously utilized, with interactivity and long-term memory that may help promote healthy lifestyles for users. No, it won’t work for everyone, but given poor adoption of healthy lifestyle factors (for example, 75% of Americans do not even fulfill the minimal recommendations for physical activity), it would be hard to think it won’t help to some extent.
There’s currently a lot of negativism about generative A.I. I get it. But the new era of primary prevention would not be possible without it. It will take time to get this to be standard of care, that is real prevention, but I believe it will ultimately wind up being seen as the most important contribution of A.I. for promoting human health.
</QUOTE>