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Artificial intelligence detects diseases ten years in advance

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Artificial intelligence detects diseases ten years in advance
An international scientific team has presented a revolutionary new technology in the field of medicine. This was reported by Zamin.uz.

Scientists have created an artificial intelligence model capable of detecting diseases that may develop in human health long in advance. Named Delphi-2M, this system analyzes over a thousand types of diseases and can sometimes identify problems ten years ahead.

The model works by integrating doctors' notes, hospital-collected data, and lifestyle factors such as smoking, diet, and physical activity. Experts emphasize that Delphi-2M indicates health risks based on probabilistic conclusions, similar to a weather forecast.

Initially trained on the UK Biobank data from the United Kingdom, the system was later tested with data from over 1.9 million citizens in Denmark's national healthcare system. Research results showed that Delphi-2M predicts dangerous diseases such as heart attacks, diabetes, and sepsis with high accuracy.

However, its capabilities in detecting random infections are currently limited. According to scientists, this technology could fundamentally transform medical practice in the future.

Doctors can use artificial intelligence to provide patients with personalized advice, recommend lifestyle changes to reduce risks, or take preventive treatment measures when necessary. It will also bring significant benefits to the entire healthcare system by enabling early detection of disease spread and allowing hospitals to plan workloads effectively.

The project was carried out in collaboration with scientists from Germany's Cancer Research Center, the European Molecular Biology Laboratory, and the University of Copenhagen. The research results were published in the prestigious journal Nature.

Experts plan to further improve the model by incorporating genetic data, blood tests, and medical imaging databases. Additionally, they aim to work with large clinical datasets covering various age groups.

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