AI tool Delphi-2M capable of foretelling the likelihood of over a thousand diseases
Article: Delphi-2M: A Revolutionary AI Model for Predicting Future Health Risks
Delphi-2M, a groundbreaking AI model, is making waves in the medical field by predicting the next disease a patient might face and the length of time until that disease will appear. This innovative technology, similar to chatbots like ChatGPT and Claude, but designed to handle medical histories rather than text, was trained on anonymized health data from nearly 2.3 million people in the UK and Denmark.
The model, described in Nature, predicts a person's likelihood of developing over 1,000 diseases and estimates when those illnesses might occur. It takes a holistic approach, simulating possible trajectories of a person's health over decades, forecasting sequences of complications, including illnesses, sleep patterns, and other aspects affecting health.
However, it's important to note that Delphi-2M reflects the biases of the datasets it was trained on. As the UK Biobank data skews towards wealthier, healthier participants, the model's accuracy varies across populations. For instance, lower scores have been observed when applied to Danish data.
In tests, Delphi-2M reached an average accuracy score (AUC) of 0.76 across hundreds of diseases in the UK dataset. While this is a promising start, the model's results come with caveats. It is not yet clear how well the model will perform for individuals with rare diseases or complex medical histories.
Delphi-2M is not a diagnostic tool at the moment, but a forecasting engine for detecting general risks and planning preventive care. Key inputs for the model include age, sex, past diagnoses spanning 1,000+ conditions, and lifestyle factors such as BMI, smoking, and alcohol use.
The potential applications of Delphi-2M are vast. More AI models like Delphi-2M could potentially be used alongside existing health calculators, offering personalized roadmaps of future risk. They could be used to predict disease risks up to 20 years in advance for both individuals and entire population groups across different countries, supporting early treatment and prevention tailored to diverse populations.
Perhaps most excitingly, Delphi-2M's projections for people at age 60 closely matched population-level outcomes a decade later, suggesting it could become a powerful tool for public health planning. Its training on UK and Danish clinical data implies potential deployment in multiple national health contexts, though its accuracy may vary for certain diseases and populations.
In conclusion, Delphi-2M represents a significant step forward in the field of AI and healthcare. As researchers continue to refine and improve the model, it has the potential to revolutionize the way we approach preventive care and public health planning.
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