The generalist medical AI will see you now
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, I 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, I’ll 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, I 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,’ the advancements made, the challenges faced, and the prospects lying ahead.
Date: 17 November 2023, 14:00 (Friday, 6th week, Michaelmas 2023)
Venue: Mathematical Institute, Woodstock Road OX2 6GG
Venue Details: Virtual
Speaker: Professor Pranav Rajpurkar (Department of Biomedical Informatics Harvard Medical School, Boston)
Organising department: Mathematical Institute
Organiser: Sara Jolliffe (University of Oxford)
Organiser contact email address: sara.jolliffe@maths.ox.ac.uk
Host: Dr Peter Minary (University of Oxford)
Part of: Mathematical Biology and Ecology
Booking required?: Not required
Audience: Members of the University only
Editor: Sara Jolliffe