Conventional genetic assessments have long operated within a binary diagnostic framework—classifying individuals as either affected or unaffected by certain diseases. However, this categorical approach inadequately captures the complexities inherent in many common conditions such as hypertension, diabetes, and various forms of cancer, where phenotypic expression can span a spectrum of severity and onset. Addressing this limitation, the Mount Sinai team employed advanced machine learning algorithms to quantify disease expression continuously, thereby providing a more refined and clinically actionable insight into penetrance. This approach transcends simplistic yes/no verdicts, offering patients and healthcare providers a dynamic and scalable risk assessment tool.

At the core of this innovation is the integration of over one million electronic health records (EHRs), which furnish the AI models with an unprecedented depth of longitudinal clinical data. Variables such as lipid profiles, complete blood counts, and markers of renal function—parameters routinely collected in clinical practice—serve as real-world physiological indicators that enrich the model’s predictive capacity. By harmonizing these diverse data streams, the AI system calculates an individualized penetrance score ranging from 0 to 1, wherein values nearing unity denote a higher probability that a particular genetic variant will precipitate disease, and values closer to zero suggest negligible or absent risk.

Senior author Dr. Ron Do, Charles Bronfman Professor in Personalized Medicine, articulates the transformative potential of this approach: “Our goal was to move beyond binary interpretations that often leave patients and clinicians uncertain about the real-world implications of genetic test results. By harnessing artificial intelligence alongside routinely available clinical laboratory data, we can now deliver more precise estimates of disease risk for patients harboring specific variants, particularly those that are rare or previously uncharacterized.” This paradigm shift promises to enhance clinical decision-making by facilitating personalized risk stratification grounded in empirical evidence rather than theoretical assumptions.

The study’s development of the “ML penetrance” score entailed rigorous data curation and algorithmic training across ten prevalent diseases. The spectrum of diseases was carefully chosen to encompass conditions with heterogenous presentation and variable genetic etiology, ensuring robust applicability of the model. When applied to over 1,600 rare genetic variants, the AI revealed unexpected patterns: some variants formerly deemed of “uncertain significance” exhibited clear associations with disease phenotypes, while others previously implicated as pathogenic showed minimal effect in population-level clinical data. These findings underscore the critical importance of leveraging large-scale, real-world datasets to revisit and refine the pathogenicity classification of genetic variants.

Lead study author Dr. Iain S. Forrest emphasizes the clinical utility of these findings, cautioning that while the AI tool is not intended to supplant physician judgment, it offers an invaluable adjunct in ambiguous cases. For instance, in carriers of variants linked to Lynch syndrome—a hereditary cancer predisposition syndrome—the penetrance score could prompt timely screening interventions in high-risk individuals, thereby preventing cancer development or enabling early detection. Conversely, a low-risk score might spare patients from unnecessary surveillance and the anxiety associated with overdiagnosis. This precision-guided approach fosters a balance between proactive care and avoidance of overtreatment.

Moreover, the investigators are expanding the scope of their model to incorporate additional diseases and a broader array of genetic alterations, including structural variants and complex haplotypes. A critical future direction involves validating the predictive accuracy longitudinally by monitoring whether individuals with high penetrance scores indeed manifest disease and assessing the impact of early clinical interventions prompted by AI-based risk assessment. Such longitudinal studies will be pivotal in solidifying the clinical integration of AI-driven penetrance estimation.

Beyond the algorithmic innovation, this research exemplifies the fruitful synergy achievable through the confluence of genomics, clinical informatics, and artificial intelligence. Mount Sinai’s Windreich Department of AI and Human Health, under the leadership of Dr. Girish N. Nadkarni, who is internationally recognized for his expertise in ethical AI deployment in healthcare, played an instrumental role in driving this interdisciplinary endeavor. The department’s commitment to responsible AI research ensures that technologies like the ML penetrance model are developed with rigorous attention to clinical applicability, patient safety, and ethical considerations.

This work also benefits from Mount Sinai’s partnership with the Hasso Plattner Institute for Digital Health, a unique collaboration between the Mount Sinai Health System and the Hasso Plattner Institute for Digital Engineering in Germany. Their combined expertise in biomedical informatics, machine learning, and digital engineering accelerates the translation of computational breakthroughs into practical clinical tools, fostering scalable innovations geared toward improving health outcomes globally.

The broader institutional context is equally significant. The Icahn School of Medicine at Mount Sinai, one of the preeminent academic medical centers in the United States, boasts extensive expertise in translational research and clinical care. Its integration within a large, diverse health system provides unparalleled access to rich clinical datasets, enabling the development of data-driven approaches such as the ML penetrance model on a population scale. This infrastructure is essential for validating AI models across heterogeneous patient populations and ensuring their generalizability and equity.

In an era when the volume of genetic testing continues to surge, yielding a vast number of rare and ambiguous variants awaiting clinical interpretation, the integration of AI-driven penetrance estimation represents a crucial advancement. This methodology has the potential to demystify genetic risk, foster precision interventions, and ultimately improve patient outcomes through data-driven personalization. As genetic medicine moves toward this more refined, continuous risk assessment paradigm, patients and clinicians alike stand to gain clarity amidst the complexity of genomic information.

The study, titled “Machine learning-based penetrance of genetic variants,” signifies a landmark step in moving beyond traditional genetics into an era where machine learning and comprehensive clinical data converge to illuminate the nuanced realities of disease risk. By equipping healthcare providers with probabilistic tools grounded in rigorous data analysis, this research heralds a future where genetic information is no longer a source of uncertainty but a guiding beacon for tailored medical care.

Subject of Research: People

Article Title: Machine learning-based penetrance of genetic variants

News Publication Date: 28-Aug-2025

Web References: https://ai.mssm.edu/

References: Forrest IS, Vy HMT, Rocheleau G, Jordan DM, Petrazzini BO, Nadkarni GN, Cho JH, Ganapathi M, Huang K-L, Chung WK, Do R. Machine learning-based penetrance of genetic variants. Science. 2025 Aug 28.

Keywords: Genetic algorithms, Machine learning, Genetic penetrance, Precision medicine, Electronic health records, Rare genetic variants

The key advancement introduced in this study is the assignment of calibrated numeric probabilities to the algorithm’s assessments. Previously, the algorithm’s output included broad categorizations like “suspected HCM” or “high risk of HCM.” However, researchers at Mount Sinai have refined the process to give patients more concrete information—such as a specific likelihood of having HCM, which could, for example, indicate a 60% chance of the condition. Joshua Lampert, MD, who is the Director of Machine Learning at Mount Sinai Fuster Heart Hospital, emphasized the transformative potential of this detailed feedback for patients. It allows individuals who might not have been previously diagnosed with HCM to gain insights into their heart health, thereby facilitating early intervention and treatment.

The importance of such advancements cannot be overstated, especially considering that HCM affects approximately one in 200 people worldwide and remains one of the leading causes for heart transplantation. Many individuals with HCM are unaware they have the condition until symptoms arise, often when the disease has already progressed to a more severe stage. By integrating this AI tool into clinical workflows, doctors can identify high-risk individuals earlier, potentially preventing critical complications associated with HCM, such as sudden cardiac death—particularly affecting younger patients, who are often in the prime of their lives.

The research team under Lampert analyzed nearly 71,000 ECG readings collected from patients between March 2023 and January 2024. Out of these, the Viz HCM algorithm flagged 1,522 cases as showing potential signs of HCM. To validate the findings, researchers conducted an extensive review of patient records and imaging data to establish confirmed diagnoses of HCM. The results yielded promising conclusions: the calibrated AI model effectively provided an accurate correlation between its predicted probabilities of HCM and the actual incidence of the disease among patients.

Enhancing the interpretability of AI in healthcare has become a major focus in recent years, and this study serves as a prime example of how technology can be integrated into clinical practices to improve patient care. Clinicians can leverage this calibrated risk model to prioritize patients according to their individual levels of risk, ultimately streamlining clinical workflows. This change allows healthcare providers to offer more tailored guidance during consultations, transforming how patients experience the healthcare system.

Dr. Vivek Reddy, co-senior author and Director of Cardiac Arrhythmia Services for Mount Sinai Health System, remarked on the transformative potential of these developments in clinical practice. He noted that the utilization of novel algorithms like Viz HCM could significantly enhance patient triage and risk stratification processes. This methodological sophistication underscores the increasing importance of employing advanced AI tools not just for their performance but for their capacity to improve patient outcomes and align with existing clinical practices.

In addition to enhancing patient care through a clearer understanding of individual risks, the research also emphasizes the importance of pragmatic implementation in healthcare settings. Dr. Girish N. Nadkarni, another co-senior author and Chair of the Windreich Department of Artificial Intelligence and Human Health, highlighted that successful integration of AI into medical workflows hinges on its ability to support clinical decision-making while ensuring it aligns with how healthcare is delivered. This study exemplifies a responsible approach to the integration of AI, showcasing that a calibrated model can significantly aid clinicians in managing their patient populations more effectively.

Despite the promising outcomes of this study, the research team acknowledges that further exploration is required for the broader application of this AI calibration strategy across different health systems nationwide. The next phase of research will focus on expanding the use of the calibrated model to ensure its efficacy and adaptability across diverse clinical environments. The ultimate goal is to establish a standardized method for employing AI technology and machine learning algorithms to enhance the predictability and reliability of cardiac diagnoses.

The potential implications of this study extend beyond the realm of HCM, as they pave the way for implementing AI in addressing a wide variety of conditions. As advancements continue within the AI space, cardiologists and healthcare providers are encouraged to remain vigilant and informed about the technological innovations that can be utilized to enhance patient care.

As the healthcare landscape navigates the integration of innovative AI tools, it becomes increasingly critical for medical practitioners to embrace these changes for the benefit of their patients. With the ability to provide targeted risk assessments and improved clinical workflow efficiency, the use of calibrated AI models represents a significant leap forward in the medical field. Researchers and clinicians are hopeful that this will establish a new paradigm in cardiology and beyond, effectively revolutionizing how patients are diagnosed, treated, and monitored.

The Mount Sinai Health System, renowned for its commitment to exceptional cardiovascular care, holds a pivotal role in championing such initiatives. As collaborative efforts among researchers, healthcare professionals, and technology developers continue to flourish, the mounting evidence supporting the role of AI in enhancing accuracy, efficiency, and patient engagement in healthcare will undoubtedly continue to grow.

Subject of Research: Hypertrophic cardiomyopathy (HCM) detection using AI.
Article Title: Calibration of ECG-Based Deep Learning Algorithm Scores for Patients Flagged as High Risk for Hypertrophic Cardiomyopathy.
News Publication Date: April 22, 2025.
Web References: https://www.mountsinai.org/
References: NEJM AI, 2025.
Image Credits: Reproduced with permission from NEJM AI, Lampert, 2025. Copyright 2025 Massachusetts Medical Society.

Keywords

Artificial intelligence, Hospitals, Human health, Heart disease, Risk factors, Machine tools, Cardiology, Cardiomyopathy, Electrocardiography.