Скачать или смотреть Webinar #20 - Application and Development of Foundational Models in Healthcare - Dr. Xiang Li

Abstract: Recent advances in natural language processing (NLP) and artificial general intelligence (AGI) have led to the development of powerful large language models (LLM) such as the GPT (Generative Pre-trained Transformer) series. These models are pre-trained on vast amounts of text data with human feedback and have demonstrated exceptional performance in a wide range of NLP tasks. However, two challenges remain in fully leveraging the power of LLMs and, more generally, large AGI models: the first challenge is the model adaptivity to specific domains, such as healthcare, where data has a highly heterogenous distribution compared with general domain and are difficult to be accessed. The second challenge is the integration of multiple data modalities, where in most application scenarios, the task is characterized by more than one data modality (e.g., image) beyond text. Here we will summarize our current line of work addressing these two challenges, including different schemes for prompt designing and engineering, coarse-fine domain adaptation, model localization, and our ultimate goal of developing multi-modal healthcare foundational AI.


Bio: Dr. Xiang Li is an Instructor of Investigation at the Massachusetts General Hospital and Harvard Medical School. He received his bachelor’s degree from the School of Electronic Information and Electrical Engineering at Shanghai Jiaotong University and his Ph.D. degree from the Department of Computer Science at the University of Georgia, advised by Distinguished Research Professor and AIMBE Fellow Tianming Liu. After graduation, Dr. Xiang Li joined Massachusetts General Hospital and Harvard Medical School as Research Fellow, with mentorship from Dr. James Thrall, Chairman Emeritus of MGH Department of Radiology, along with Dr. Quanzheng Li, the director of MGH/HMS Center for Advanced Medical Computing and Analysis. Dr. Xiang Li's research focus includes developing artificial intelligence solutions for analyzing healthcare data, especially fusion across imaging and non-imaging data, and developing medical informatics systems for smart data management and AI deployment in the clinical workflow. He is the founding chair of the International Workshop on Multiscale Multimodal Medical Imaging. He has received multiple funding support from NIH for his research on multi-modal imaging fusion and clinical decision support. Dr. Xiang Li’s work has been awarded by MGH Thrall Innovation Grants Award (2022), NVIDIA Global Impact Award (2018), ISBI Best Paper Award (2011, 2013 and 2020), and IEEE TRPMS Best Paper (2022).