Science<\/a>. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.<\/p>\nTraditional genetic studies often rely on a simple yes\/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don\u2019t fit neatly into binary categories. The Mount Sinai researchers trained AI models to quantify disease on a spectrum, offering more nuanced insight into how disease risk plays out in real life.<\/p>\n
\u201cWe wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means,\u201d says\u00a0Ron Do, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. \u201cBy using artificial intelligence and real-world lab data, such as cholesterol levels or blood counts that are already part of most medical records, we can now better estimate how likely disease will develop in an individual with a specific genetic variant. It\u2019s a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings.\u201d<\/p>\n
Using more than 1 million electronic health records, the researchers built AI models for 10 common diseases. They then applied these models to people known to have rare genetic variants, generating a score between 0 and 1 that reflects the likelihood of developing the disease.<\/p>\n
A higher score, closer to 1, suggests a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk. The team calculated \u201cML penetrance\u201d scores for more than 1,600 genetic variants.<\/p>\n
Some of the results were surprising, say the investigators. Variants previously labeled as \u201cuncertain\u201d showed clear disease signals, while others thought to cause disease had little effect in real-world data.<\/p>\n
\u201cWhile our AI model is not meant to replace clinical judgment, it can potentially serve as an important guide, especially when test results are unclear. Doctors could in the future use the ML penetrance score to decide whether patients should receive earlier screenings or take preventive steps, or to avoid unnecessary worry or intervention if the variant is low-risk,\u201d says lead study author Iain S. Forrest,\u00a0MD, PhD,\u00a0in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. \u201cIf a patient has a rare variant associated with Lynch syndrome, for instance, and it scores high, that could trigger earlier cancer screening, but if the risk appears low, jumping to conclusions or overtreatment might be avoided.\u201d<\/p>\n
The team is now working to expand the model to include more diseases, a wider range of genetic changes, and more diverse populations. They also plan to track how well these predictions hold up over time, whether people with high-risk variants actually go on to develop disease, and whether early action can make a difference.<\/p>\n
\u201cUltimately, our study points to a potential future where AI and routine clinical data work hand in hand to provide more personalized, actionable insights for patients and families navigating genetic test results,\u201d says Dr. Do. \u201cOur hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means.\u201d<\/p>\n
Republished courtesy of Mount Sinai Hospital<\/a><\/p>\n\u00a0<\/p>\n
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