AI Model Revolutionizes Genetic Disease Risk Prediction

New AI model predicts which genetic mutations truly drive disease

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk. Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don't 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. "We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means," says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. "By 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's a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings." 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. 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 "ML penetrance" scores for more than 1,600 genetic variants. Some of the results were surprising, say the investigators. Variants previously labeled as "uncertain" showed clear disease signals, while others thought to cause disease had little effect in real-world data. "While 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," says lead study author Iain S. Forrest, MD, PhD, in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. "If 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." 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. "Ultimately, 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," says Dr. Do. "Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means." The paper is titled "Machine learning-based penetrance of genetic variants." The study's authors, as listed in the journal, are Iain S. Forrest, Ha My T. Vy, Ghislain Rocheleau, Daniel M. Jordan, Ben O. Petrazzini, Girish N. Nadkarni, Judy H. Cho, Mythily Ganapathi, Kuan-Lin Huang, Wendy K. Chung, and Ron Do. This work was supported in part by the following grants: National Institute of General Medical Sciences of the National Institutes of Health (NIH) (T32-GM007280); the National Institute of General Medical Sciences of the NIH (R35-GM124836); the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429); the National Human Genome Research Institute of the NIH (R01-HG010365); the National Institute of General Medical Sciences of the NIH (R35-GM138113); and the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429).

AI and routine lab tests offer a more accurate prediction of genetic disease risk

Mount Sinai Health SystemAug 28 2025 When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk. Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don't 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. We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means. By 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's a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings." Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai 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. 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 "ML penetrance" scores for more than 1,600 genetic variants. Some of the results were surprising, say the investigators. Variants previously labeled as "uncertain" showed clear disease signals, while others thought to cause disease had little effect in real-world data. "While 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," says lead study author Iain S. Forrest, MD, PhD, in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. "If 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." 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. "Ultimately, 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," says Dr. Do. "Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means." Mount Sinai Health System Journal reference: Forrest, I. S., et al. (2025) Machine learning-based penetrance of genetic variants. Science. doi.org/10.1126/science.adm7066

AI and lab tests combine to predict disease risk from rare genetic variants

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in Science. The paper is titled "Machine learning-based penetrance of genetic variants." Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk. Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don't 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. "We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means," says Ron Do, Ph.D., senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. "By 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's a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings." 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. 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 "ML penetrance" scores for more than 1,600 genetic variants. Some of the results were surprising, say the investigators. Variants previously labeled as "uncertain" showed clear disease signals, while others thought to cause disease had little effect in real-world data. "While 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," says lead study author Iain S. Forrest, MD, Ph.D., in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. "If 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." 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. "Ultimately, 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," says Dr. Do. "Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means."

Scientists create AI tool to predict risk for hereditary diseases

The researchers say the model could be used to help doctors decipher the results of genetic tests and funnel patients into the appropriate level of care. US researchers have developed an artificial intelligence (AI) tool to better predict whether rare genetic mutations will lead to disease, with the goal to speed up early detection and avoid unnecessary treatments. Genetic testing can identify changes, or variants, in a person's DNA - but many have little or no impact on our health. A single variant rarely provides the full picture, either, given that multiple genes, along with their interactions and environmental factors, affect our risk of conditions ranging from heart disease to cancer. The New York-based research team aimed to make that picture clearer. They developed a tool that uses AI and electronic medical records - which contain information on a patient's health history - to predict the likelihood that people will develop diseases based on their genetic risks. "We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means," said Ron Do, one of the study's authors and a professor of personalised medicine at the Icahn School of Medicine at Mount Sinai. "By 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," Do added in a statement. The researchers used more than one million electronic health records to develop AI models for 10 inherited conditions, including breast cancer and polycystic kidney disease (PKD). They applied these models to patients with rare genetic variants, assigning them a score between 0 and 1 to determine their likelihood of developing the disease. This enabled the researchers, who published their findings in the journal Science, to calculate a risk score for more than 1,600 genetic variants. The model has already offered clues about the health risks associated with certain genetic mutations. For example, some variants that had been labelled as "uncertain" had clear connections to specific diseases. "While our AI model is not meant to replace clinical judgment, it can potentially serve as an important guide, especially when test results are unclear," said Dr Iain Forrest, who works in Do's lab and is the study's lead author. Doctors could use the risk score to decide whether patients should undergo additional screenings, take steps to try to prevent a disease from developing - or "avoid unnecessary worry or intervention if the variant is low-risk," Forrest said in a statement. The researchers are now expanding the model to include more diseases and genetic variants as well as a more diverse group of patients. "Ultimately, our study points to a potential future where AI and routine clinical data work hand in hand to provide more personalised, actionable insights for patients and families navigating genetic test results," Do said.

Mount Sinai Researchers Use AI and Lab Tests to Predict Genetic Disease Risk | Newswise

Newswise -- New York, NY [August 28, 2025] -- When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers at the Icahn School of Medicine at Mount Sinai have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance. The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the August 28 online issue of Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk. Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don't 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. "We wanted to move beyond black-and-white answers that often leave patients and providers uncertain about what a genetic test result actually means," says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at the Icahn School of Medicine at Mount Sinai. "By 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's a much more nuanced, scalable, and accessible way to support precision medicine, especially when dealing with rare or ambiguous findings." 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. 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 "ML penetrance" scores for more than 1,600 genetic variants. Some of the results were surprising, say the investigators. Variants previously labeled as "uncertain" showed clear disease signals, while others thought to cause disease had little effect in real-world data. "While 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," says lead study author Iain S. Forrest, MD, PhD, in the lab of Dr. Do at the Icahn School of Medicine at Mount Sinai. "If 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." 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. "Ultimately, 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," says Dr. Do. "Our hope is that this becomes a scalable way to support better decisions, clearer communication, and more confidence in what genetic information really means." The paper is titled "Machine learning-based penetrance of genetic variants." The study's authors, as listed in the journal, are Iain S. Forrest, Ha My T. Vy, Ghislain Rocheleau, Daniel M. Jordan, Ben O. Petrazzini, Girish N. Nadkarni, Judy H. Cho, Mythily Ganapathi, Kuan-Lin Huang, Wendy K. Chung, and Ron Do. This work was supported in part by the following grants: National Institute of General Medical Sciences of the National Institutes of Health (NIH) (T32-GM007280); the National Institute of General Medical Sciences of the NIH (R35-GM124836); the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429); the National Human Genome Research Institute of the NIH (R01-HG010365); the National Institute of General Medical Sciences of the NIH (R35-GM138113); and the National Institute of Diabetes and Digestive and Kidney Diseases (U24-DK062429). About Mount Sinai's Windreich Department of AI and Human Health Led by Girish N. Nadkarni, MD, MPH -- an international authority on the safe, effective, and ethical use of AI in health care -- Mount Sinai's Windreich Department of AI and Human Health is the first of its kind at a U.S. medical school, pioneering transformative advancements at the intersection of artificial intelligence and human health. The Department is committed to leveraging AI in a responsible, effective, ethical, and safe manner to transform research, clinical care, education, and operations. By bringing together world-class AI expertise, cutting-edge infrastructure, and unparalleled computational power, the department is advancing breakthroughs in multi-scale, multimodal data integration while streamlining pathways for rapid testing and translation into practice. The Department benefits from dynamic collaborations across Mount Sinai, including with the Hasso Plattner Institute for Digital Health at Mount Sinai -- a partnership between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System -- which complements its mission by advancing data-driven approaches to improve patient care and health outcomes. At the heart of this innovation is the renowned Icahn School of Medicine at Mount Sinai, which serves as a central hub for learning and collaboration. This unique integration enables dynamic partnerships across institutes, academic departments, hospitals, and outpatient centers, driving progress in disease prevention, improving treatments for complex illnesses, and elevating quality of life on a global scale. In 2024, the Department's innovative NutriScan AI application, developed by the Mount Sinai Health System Clinical Data Science team in partnership with Department faculty, earned Mount Sinai Health System the prestigious Hearst Health Prize. NutriScan is designed to facilitate faster identification and treatment of malnutrition in hospitalized patients. This machine learning tool improves malnutrition diagnosis rates and resource utilization, demonstrating the impactful application of AI in health care. For more information on Mount Sinai's Windreich Department of AI and Human Health, visit: ai.mssm.edu About the Hasso Plattner Institute at Mount Sinai At the Hasso Plattner Institute for Digital Health at Mount Sinai, the tools of data science, biomedical and digital engineering, and medical expertise are used to improve and extend lives. The Institute represents a collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System. Under the leadership of Girish Nadkarni, MD, MPH, who directs the Institute, and Professor Lothar Wieler, a globally recognized expert in public health and digital transformation, they jointly oversee the partnership, driving innovations that positively impact patient lives while transforming how people think about personal health and health systems. The Hasso Plattner Institute for Digital Health at Mount Sinai receives generous support from the Hasso Plattner Foundation. Current research programs and machine learning efforts focus on improving the ability to diagnose and treat patients. About the Icahn School of Medicine at Mount Sinai The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the seven member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to New York City's large and diverse patient population. The Icahn School of Medicine at Mount Sinai offers highly competitive MD, PhD, MD-PhD, and master's degree programs, with enrollment of more than 1,200 students. It has the largest graduate medical education program in the country, with more than 2,600 clinical residents and fellows training throughout the Health System. Its Graduate School of Biomedical Sciences offers 13 degree-granting programs, conducts innovative basic and translational research, and trains more than 560 postdoctoral research fellows. Ranked 11th nationwide in National Institutes of Health (NIH) funding, the Icahn School of Medicine at Mount Sinai is among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. More than 4,500 scientists, educators, and clinicians work within and across dozens of academic departments and multidisciplinary institutes with an emphasis on translational research and therapeutics. Through Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. -------------------------------------------------------