AI could help identify people at higher risk of common cancers using routine health data
Machine learning models using routinely collected health information, such as blood tests, medical history and lifestyle factors, could help identify people at higher risk of several common cancers, according to an international study reported at the ESMO Congress 2026.
An international study presented at the ESMO Congress 2026 suggests that machine learning models using routine health data could help identify individuals at higher risk of developing several common cancers. The research, conducted using data from UK Biobank participants followed for approximately 18 years, developed around 500 models to predict cancer risk across 20 common cancers.
The models outperformed traditional statistical approaches, with the best performance for lung, esophageal, and liver cancers. However, performance varied across cancer types, with more limited predictions for ovarian, breast, and cervical cancers. The study also developed PRE-Screen, a toolbox enabling AI agents to run the risk models consistently, potentially serving as a low-cost pre-screening step using existing routine care data.
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