Seminars

Abstract:  Pandora’s box has opened in the form of publicly available generative AI systems for every imaginable (and many unintended) purposes. With a global scarcity of medical expertise against the unlimited demand of people in need, AI's potential to democratize healthcare knowledge, access, and to recover efficiencies is desperately needed. The implications are vast as we converge upon a point in history where human vs. computer generated content can no longer be reliably distinguished. This session will review the attention and intention required for AI applications in the high-stakes world of healthcare as we distinguish real magic from convincing illusions.

Bio: Jonathan H. Chen MD, PhD leads a clinical informatics research group to empower individuals with the collective experience of the many, combining human and artificial intelligence to deliver better care than either. Dr. Chen founded a company to translate his Computer Science graduate work into an AI system still used by students around the world. His expertise is featured in the popular press with over 100 research publications and awards. Dr. Chen continues to practice medicine for the reward of caring for real people and to inspire his research to discover and distribute the latent knowledge embedded in clinical data.

Abstract: This study evaluates GPT-4's effectiveness in annotating medical notes to identify COVID-19 admissions, leveraging a dataset from the Consortium for Clinical Characterization of COVID-19 (4CE). Across multiple institutions and languages, GPT-4 demonstrated a high concordance with clinicians' validations, showing 77% agreement in a variety of clinical questions. The research highlights GPT-4's potential in accurately identifying patients for study enrollment, showcasing its proficiency in extracting explicit information and inferring implicit details from medical notes. Despite some challenges in deeper inference tasks, the findings advocate for the promising role of LLMs in streamlining clinical documentation and supporting healthcare research, indicating significant avenues for future advancements in AI applications in medicine.

Bio: Dr. Amelia Li Min Tan, PhD, leads research at Harvard Medical School, focusing on the intersection of biomedical informatics and personalized medicine across various medical topics. Her leadership in global consortia, such as the 4CE, reflects her commitment to transforming health insights into clinical actions. Holding a PhD from the National University of Singapore, Dr. Tan mentors students and drives innovation in healthcare through data science. Her work, featured in numerous publications, demonstrates her dedication to leveraging computational tools for health advancements.

Abstract: Dr. Bernhardt talk will overview new resources, tools, and analytics to study the human brain across multiple spatial scales. He will illustrate how these approaches can help to understand the functional organization of specific regions, such as the hippocampus, and the role of large-scale systems, such as the default mode network, in the human brain.

Bio: Boris Bernhardt is Associate Professor of Neurology and Neurosurgery and a Canada Research Chair at the Montreal Neurological Institute, McGill University, in Montreal, Quebec, Canada.
His lab studies the role of structural and functional network organization in human cognition in neurotypical individuals and populations with atypical brain development, notably people with epilepsy and autism. To this end, they develop neuroinformatics approaches that integrate connectome models with multimodal neuroimaging, histology, and transcriptomics techniques.

Abstract: We are in the midst of an AI hypecycle. Large language models are everywhere you look – and they are increasingly being integrated into biomedical research and clinical care.  The potential upside for AI is sky high and yet, haven’t we been here before?  In this seminar, I will review why AI, and particularly machine learning, has become the technology du jour in biomedicine - again.  I will provide illustrations of machine learning in support of novel biomedical discovery, including drug repurposing and automation in clinical phenotyping.  At the same time, I will review how blind trust in AI can lead to numerous societal dilemmas, including violations of privacy, algorithmic unfairness, and an overall loss of trust.  I will then show how these problems be represented in AI development and application lifecyle, so that problems can be spotted and addressed before they destroy our research ecosystems and clinical operations.

Bio: Bradley Malin, Ph.D. is the Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science at Vanderbilt University Medical Center. He co-founded and co-directs ADVANCE Center, which is focused on the development of foundational AI models, their translation into biomedical research and clinical practice, and continuous monitoring and surveillance.  He is a principal investigator of the Instructure Core of the NIH AIM-AHEAD Program and the Ethical and Trustworthy AI Core of the NIH Bridge2AI Center.  He is a member of the Board of Scientific Counselors of the National Center for Health Statistics (NCHS) of the U.S. Centers for Disease Control and Prevention (CDC), as well as the Technical Anonymisation Group (TAG) of the European Medicines Agency.  Among various honors, he is an elected fellow of the National Academy of Medicine and was a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE)