Allen School professor Su-In Lee, who directs the University of Washington’s AI for bioMedical Sciences (AIMS) Lab, is shaping the future of biology and medicine through artificial intelligence. Her research focuses on fundamentally advancing AI and machine learning (ML) techniques to provide insights into complex biological systems and drive healthcare breakthroughs, transforming fields from basic biology to clinical medicine, including cancer biology, dermatology and critical care.
Lee’s groundbreaking work has earned her a long list of accolades. Most recently, the International Society for Computational Biology (ISCB) inducted her into its 2025 Class of Fellows. These fellows lead the field with their innovative research and service, reflecting “a career of significant impact and a dedication to the scientific community.”
“I am really grateful and honored to be named an ISCB Fellow,” said Lee, who holds the Paul G. Allen Professorship in Computer Science & Engineering. “This recognition fuels my desire to contribute further and push the field to the next stage.”
The cover illustrates that combining the expertise of physicians to identify medically relevant features in dermatology images with generative machine learning enables auditing of medical-image classifiers.
Lee’s research on using generative AI and physician expertise to audit medical-image classifiers was recently featured on the cover of Nature Biomedical Engineering.
One of Lee’s most pivotal contributions to the field is her work on the SHAP, or SHapley Additive exPlanations, values. The technique uses a game theory approach to help explain the output of an ML model. Her research using SHAP techniques tackles the accuracy versus interpretability problem. Simpler models can be more interpretable at the expense of being less accurate, but complex models are more accurate but difficult to interpret. Instead, Lee and her collaborators balance the two to develop high-performance, expressive models that are effective for biomedicine.
Using SHAP values as a foundation, Lee and her collaborators developed a novel framework called Prescience that could predict and also explain a patient’s risk of developing hypoxemia during surgery. Their follow-up research CoAI, also known as Cost-Aware Artificial Intelligence, used SHAP values to reduce the time, effort and resources needed to predict patient outcomes and inform the treatment plan. Lee has also used SHAP values to understand factors influencing aging to better treat age-related disorders using the ENABL Age, or ExplaiNAble BioLogical Age, framework.
