Dr. Peng Zhang
Huazhong University of Science and Technology
Peng Zhang received the B.S. degree in biomedical engineering and the Ph.D. degree in control science and engineering from Huazhong University of Science and Technology, Wuhan, China, in 2011 and 2018, respectively. He joined Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, as a post-doctoral fellow in 2018. His research areas include intracortical brain-machine interface, biomedical signal analysis, and deep learning. He has published 25 SCI papers in high-level journals such as Med, Nature Chemistry, and IEEE Trans. He also authored a chapter in the book "Neural Interface: Frontiers and Applications" and applied for 16 patents (8 granted and one patent transformation with 1.27 million).
Speech title: “Automatic Detection and Risk Prediction of Atrial Fibrillation”
Abstract: Atrial fibrillation is the most common arrhythmia in the general population, and can lead to dangerous complications. Effective automatic detection and risk prediction of atrial fibrillation are crucial for its prevention and treatment. This presentation will first introduce the research on atrial fibrillation detection, which involves developing a deep learning-based AI algorithm to accurately and automatically detect atrial fibrillation episodes from clinical 24-hour Holter ECG data. Subsequently, the presentation will present the research on atrial fibrillation risk prediction, where a deep learning-based AI algorithm is developed to effectively predict individual atrial fibrillation risks using only heartbeat information during sinus rhythm. Both studies have undergone comprehensive performance evaluations on large-scale real-world clinical datasets. Additionally, the presentation will explore how clinicians can utilize these AI tools to enhance their atrial fibrillation detection and risk prediction capabilities in real clinical practice.
Dr. Yvonne Leung
Northeastern University, USA & University of Toronto, Canada
Dr. Yvonne Leung is a multifaceted researcher and educator with extensive experience in psychosocial oncology, mental health, and healthcare analytics. She currently holds positions as an Assistant Teaching Professor in the Analytics program at Northeastern University, a Scientist at the University Health Network (UHN), and an Adjunct Lecturer at the Department of Psychiatry, University of Toronto. With a Ph.D. in Kinesiology and Health Science from York University, specializing in Health Psychology, Dr. Leung has over 15 years of experience in psychosocial and mental health research. She has secured more than C$1.3 million in research funding and awards, publishing 49 peer-reviewed journal articles and presenting at over 70 conferences. Dr. Leung's research focuses on innovative applications of artificial intelligence in healthcare. She leads projects using deep learning-based natural language processing algorithms to develop chatbot solutions for automated self-care support, particularly for cancer patients. Her work aims to improve quality of life and access to care for patients, especially those with metastatic breast cancer. At Northeastern University, Dr. Leung teaches courses in the Analytics program, bringing her expertise in advanced statistical techniques. She also conduct workshops on building agents and Retrieval Augmented Generation Chatbots with open-source large and small language models.
Speech title: “Artificial Intelligence Based Patient Librarian”
Abstract: Few online interventions meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC) patients with HR+/ HER2- subtypes. The current report describes the development and evaluation of the Artificial Intelligence Patient Librarian (AIPL), an interactive chatbot to deliver patient education and navigation by leveraging curated resources at the Princess Margaret Cancer Center. AIPL offered conversational patient education about the disease, invited users to ask questions on topics of interest, and provided tailored online resource recommendations. A mixed-method study assessed the impact of AIPL on patient ability to manage the advanced disease. The study consisted of 3 Phases: 1. Educational content transformed to be delivered by the chatbot, annotating over 100 credible online resources to drive recommendations using a Convolution Neural Network (CNN). 2. Beta-testing of the chatbot with 42 participants who completed a pre-survey, used AIPL for two weeks, and then completed a post-survey, both measuring patient activation. Patient activation measure (PAM) assessed their skill, knowledge, and confidence in managing their health. Post-survey also assessed user experience of the AIPL using the System Usability Scale (SUS). 3. Focus groups exploring user experiences. Of 42 (70%) consenting participants, 36 (85.7%) completed the study, and 10 (23.8%) participated in focus groups. The majority of the participants were aged 40-64 years. No significant differences were observed in PAM scores between pre-survey (mean=59.33 , SD=5.19) and post-survey (mean=59.22, SD=6.16). SUS scores indicated good usability. Thematic analysis identified four themes describing the extent to which technology impacted their management of metastatic breast cancer: 1. AIPL offers basic guidance on wellness and health, 2. AIPL provides limited support for managing relationships, 3. AIPL offers limited medical information unique to their conditions, and 4. AIPL is unable to offer hope to patients. Although AIPL showed no impact on PAM, possibly due to high baseline activation, it demonstrated good usability and addressed basic information needs, especially for newly diagnosed MBC patients. Future work will incorporate a large language model (LLM) in the AIPL to ensure that patients receive more comprehensive and personalized assistance.
Assoc. Prof. LEE John Sie Yuen
City University of Hong Kong