The Impact of AI in Healthcare

About the Speaker:

Dr. Peter Fitzgerald is the Director of the Center for Cardiovascular Technology and the Cardiovascular Core Analysis Laboratory (CCAL) at Stanford University School of Medicine. He is an interventional cardiologist and holds a PhD in engineering. He is a professor in both the Department of Medicine and the Department of Engineering (by courtesy) at Stanford.

Dr. Fitzgerald has led or participated in more than 175 clinical trials, has published more than 550 manuscripts and book chapters, and lectures worldwide. Over the past two decades, he has trained more than 150 postdoctoral researchers in engineering and medicine. Most recently, Dr. Fitzgerald completed two years of study toward a master’s degree focused on machine learning and agentic principles supporting AI.

Dr. Fitzgerald has been a principal founder of 24 medical startups in the San Francisco Bay Area and has transitioned 17 of them to large medical device companies. He serves on several boards of directors and advises dozens of medical device startups, as well as multinational healthcare companies, on the design and development of new diagnostic and therapeutic devices in the cardiovascular arena. In 2001, Dr. Fitzgerald was part of the founding team of LVP Capital, a venture firm focused on medical device and biotechnology startups in San Francisco. In 2010, he co-founded Triventures, an early-stage medical technology fund with offices in Palo Alto and Tel Aviv.

About the Program:

Healthcare has faced challenges in recent years from regulation, litigation, and insurance ambiguity, increasing the gap between the supply and demand for care. Additionally, the aging population with co-morbidities stresses the need for continuity of care, which is desperately inefficient.

AI is being rushed in with impunity and overvaluations in many vertical businesses. Healthcare holds more than 50% of the world’s data (both structured and unstructured) and are a suitable setting for tools for reinforcement learning and agentic AI. This includes scheduling efforts, conversational bots, standardization of care, information co-pilots, revenue cycle management, and security protection. As data accumulates and directionality becomes more uniform, the right patient for the right technology at the right time may be enhanced. Furthermore, as this data grows larger, devices become smaller, facilitating access to rural care and continuity of complex care from hospitals to homes. Healthcare professionals are assisted by AI, expanding their reach and providing continuity of care while ultimately making decisions regarding such care. AI is an accelerant and not a product, but it will have a major impact on many facets of healthcare, which is critically needed today.