Presentation Overview:

Five decades have passed in the evolution of Artificial Intelligence in Medicine (AIM), a field that has evolved substantially while tracking the corresponding changes in computer science, hardware technology, communications, and biomedicine. Emerging from medical schools and computer science departments in its early years, the AIM field is now more visible and influential than ever before, paralleling the enthusiasm and accomplishments of AI and data science more generally.  This talk will briefly summarize some of AIM history, providing an update on the status of the field as we enter our second half-century. The inherent complexity of medicine and of clinical care necessitates that we address not only decision-making performance but also issues of usability, workflow, transparency, safety, and the pursuit of persuasive results from formal clinical trials. These requirements contribute to an ongoing investigative agenda for AIM research and development.

Speaker:

Edward H. Shortliffe, MD, PhD, FACMI,
Chair Emeritus & Adjunct Professor of Biomedical Informatics, Columbia University, NY

Ted Shortliffe is Chair Emeritus and Adjunct Professor of Biomedical Informatics at Columbia University’s Vagelos College of Physicians and Surgeons.  He also holds adjunct appointments at Arizona State University and Weill Cornell Medical College.  Editor Emeritus of the Journal of Biomedical Informatics (Elsevier), he is also editor of the Springer textbook Biomedical Informatics: Computer Applications in Health Care and Biomedicine, just released in its 5th edition. In the past Dr. Shortliffe served as President/CEO of AMIA and was founding Dean of the University of Arizona College of Medicine in Phoenix. He has spearheaded the formation and evolution of graduate degree programs in biomedical informatics at Stanford, Columbia, and Arizona State Universities.  Both a PhD computer scientist and a physician who has practiced internal medicine, Dr. Shortliffe is an elected member of the National Academy of Medicine, a fellow of the American College of Medical Informatics and of the Association for the Advancement of Artificial Intelligence, and a Master of the American College of Physicians. He received the Association of Computing Machinery’s Grace Murray Hopper Award in 1976 and ACMI’s Morris F. Collen Award in 2006.

Objectives for CE:

After attending the webinar, participants should be able to:

• Understand the historical development of AI applications in medicine and the key methods on which the field has been built
• Identify three key types of clinical decision support systems and the requirements for their implementation and evaluation
• Specify the importance of explainability and thereby recognize the potential for future efforts to combine traditional knowledge-based approaches with today’s machine learning methods in medical applications