Wednesday, November 5, 2025
5:00PM-7:30PM
Venue
The New York Academy of Medicine
1216 Fifth Avenue at 103rd Street
New York, NY 10029
$25 Registration fee; $10 Students
Refreshments will be provided.
Register
Effective care of acutely ill patients requires assessment and diagnostic skills and attentive monitoring of their clinical status. These skills are not the property of any one clinical discipline. Recent research has reported advances on the use of AI and large language models (LLM) to enhance practitioner effectiveness in these areas. A multidisciplinary program designed to be of interest to all healthcare practitioners, will offer internationally recognized experts who will illuminate the advances and challenges posed by these applications in practice.
The panel presentations will highlight recent advances in the use of AI to enhance the accuracy of clinical diagnosis and to accelerate recognition of emergent clinical deterioration among hospitalized patients. They will illuminate the research supporting these developments. They will also emphasize the challenges inherent in bringing them to bear on real-world practice. Attendees will be provided tips on how to identify and navigate trustworthy AI resources in practice.
The program will be preceded by an expo featuring live exhibits and demonstrations of available and developing AI resources. Refreshments will be available to attendees.
Abstract submissions must follow these guidelines: Include a title, list of authors with affiliations, and a structured abstract (maximum 500 words) detailing purpose, methods, results, and conclusions/implications. Please find more information below:
Agenda
5:00–6:00 PM – Expo & Reception (including poster sessions and hands-on demonstrations of AI products and programs)
6:00–7:30 PM – Speaker Panel
Speakers
Kenrick Cato, PhD, RN, CPHIMS, FAAN
Kenrick Cato, PhD, RN, CPHIMS, FAAN, is a clinical informatician whose research focuses on mining electronic patient data to support decision-making for clinicians, patients, and caregivers. Operationally, he spends his time mining and modeling Nursing data to optimize Nursing value in Healthcare. He is also involved in several national-level informatics organizations, including as a board member of the American Medical Informatics Association (AMIA), Chair of the Nursing Informatics Working Group(NIWG) of AMIA, as well as a convening member of the AMIA-sponsored 25 x 5 initiative to reduce documentation burden. Dr. Cato received his BSN, MS, and Ph.D. in Clinical informatics at Columbia University. He is co-principal investigator on the Communicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System.
Geoff Norman
Geoff Norman is Professor Emeritus of the Department of Clinical Epidemiology and Biostatistics at McMaster University. He received a Ph.D. in nuclear physics from McMaste in 1971, and a M.A. in educational psychology from Michigan State in 1977.
He has a long-standing interest is in cognitive psychology applied to clinical reasoning, learning, and decision-making.. He has published over 300 peer-reviewed papers and over 30 books and book chapters and has received numerous national and international awards.
Dr. Matthew Sibbald MD MHPE PhD FRCPC
Dr. Matthew Sibbald MD MHPE PhD FRCPC is an interventional cardiologist, Associate Dean of Undergraduate Medical Education, and educational scientist at McMaster University. He holds a master’s and PhD from Maastricht University, Netherlands in Health Professions Education. He leads Canada’s largest three-year MD program, with a focus on integrating AI. His scholarship includes research on cognitive load, clinical judgment, and the use of AI in practice and assessment.
Ashley Beecy MD, FACC
Ashley Beecy MD, FACC is currently the Chief AI Officer for Sutter Health in California. Previously she served as Medical Director of Artificial Intelligence (AI) Operations at New York Presbyterian, where she was responsible for the governance, evaluation and implementation of clinical AI models. She is also a practicing cardiologist. Her research focus is in digital health, including the safe and effective use of AI in healthcare. Dr. Beecy earned her medical degree from Albert Einstein College of Medicine and completed her Internal Medicine residency at New York Presbyterian/Weill Cornell Medicine. Prior to a career in medicine, Dr. Beecy spent 10 years working in industry. As an electrical engineer at IBM, she was responsible for the physical design of chip level architectures.