Abstract: Artificial intelligence (AI) is transforming the landscape of cancer research and clinical diagnosis. Recent advances in microscopic image digitization, multi-modal machine learning algorithms, and scalable computing infrastructure have paved the way for AI-enhanced pathology assessments. In this talk, I will highlight recent breakthroughs in pathology foundation models and their effectiveness in analyzing high-resolution digital pathology images. In addition, I will present examples of AI-empowered real-time pathology evaluations during cancer surgery and demonstrate their adaptability to evolving diagnostic classifications. Furthermore, I will discuss recent studies that employed AI to reveal intriguing links between cell morphology and molecular profiles. Finally, I will outline ongoing challenges in developing robust medical AI systems and identify research directions to address these critical issues.
Bio: Kun-Hsing “Kun” Yu, M.D, Ph.D. is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School. He developed the first fully automated artificial intelligence (AI) algorithm to extract thousands of features from whole-slide histopathology images, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and successfully identified previously unknown cellular morphologies associated with patient prognosis. His lab integrates cancer patients’ multi-omics (genomics, epigenomics, transcriptomics, and proteomics) profiles with quantitative histopathology patterns to predict their clinical phenotypes. More than 30 research laboratories worldwide have independently validated the AI methods developed in the Yu Lab.