Artificial Intelligence & Machine Learning for Diagnostics, Early Detection & Personalized Medicine

Research on AI for health has immense potential to transform healthcare. We are developing AI technologies that can improve disease diagnosis, detect early signs of illness, help understand the relationships among biology, behavior, and health, and recommend personalized treatment plans. These advancements would enhance accuracy, explainability, and safety of healthcare, and enable the discovery of new diagnostic and treatment methods.

Dr. Khurram Nasir’s research at the intersection of artificial intelligence (AI), machine learning (ML), cardiometabolic health, and aging is pioneering new ways to understand and combat chronic diseases that affect millions globally. His work leverages advanced computational techniques to analyze complex biomedical data, aiming to improve disease prediction, prevention, and management.

Cardiometabolic diseases, such as heart disease, diabetes, and stroke, are leading causes of morbidity and mortality worldwide. These conditions often develop gradually over many years and are influenced by a multitude of genetic, environmental, and lifestyle factors. Dr. Nasir’s research employs AI and ML algorithms to integrate diverse datasets, including electronic health records, imaging data, and genomics, to uncover subtle patterns and risk factors that traditional analyses might miss.

A key focus of Dr. Nasir’s work is on aging populations, where the burden of cardiometabolic diseases is particularly high. Aging brings complex physiological changes that interact with disease processes, making early detection and personalized interventions critical. By applying ML models to longitudinal data, his team can identify early markers of disease progression and stratify patients based on individualized risk profiles.