Topic: The generalist medical AI will see you now
Speaker: Assistant Professor Pranav Rajpurkar, Department of Biomedical Informatics, Harvard University (Harvard)
Date and time: April 22, 10:00–11:30
Venue: Room 103, BME Building
Host: Cui Zhiming
Abstract:
Accurate interpretation of medical images is crucial for disease diagnosis and treatment, and AI has the potential to minimize errors, reduce delays, and improve accessibility. The focal point of this presentation lies in a grand ambition: the development of ‘Generalist Medical AI’ systems that can closely resemble doctors in their ability to reason through a wide range of medical tasks, incorporate multiple data modalities, and communicate in natural language. Starting with pioneering algorithms that have already demonstrated their potential in diagnosing diseases from chest X-rays or electrocardiograms, matching the proficiency of expert radiologists and cardiologists, Dr. Rajpurkar will delve into the core challenges and advancements in the field. The discussion will navigate towards the topic of label-efficient AI models: with a scarcity of meticulously annotated data in healthcare, the development of AI systems capable of learning effectively from limited labels has become a key concern. In this vein, Dr. Rajpurkar will delve into how the innovative use of self-supervision and pre-training methods has led to algorithmic advancements that can perform high-level diagnostic tasks using significantly less annotated data. Additionally, he will talk about initiatives in data curation, human-AI collaboration, and the creation of open benchmarks to evaluate the generalizability of medical AI algorithms. In sum, this talk aims to deliver a comprehensive picture of the state of ‘Generalist Medical AI’, with the advancements made, the challenges faced, and the prospects lying ahead.
Biography:
Pranav Rajpurkar is an Assistant Professor at the Department of Biomedical Informatics, Harvard University. His research focuses on developing AI systems that can interpret medical data, reason through complex problems, and communicate at an expert level, with the goal of creating AI doctors that can work independently or alongside human physicians. Rajpurkar has published over 100 academic articles in journals like Nature, NEJM, and Nature Medicine, garnering more than 25,000 citations in prestigious journals. He has been recognized with numerous awards, including Forbes 30 Under 30 in science in 2022, MIT Tech Review’s Innovator Under 35 in 2023, and Google Research Scholar.