Name
Panel 1: Developing generative AI systems for healthcare
Date & Time
Friday, September 20, 2024, 9:15 AM - 10:45 AM
Speakers
Atlas Wang, University of Texas at Austin Genai-based Chatbot For Early Dementia Intervention
Pranav Rajpurkar, Harvard Medical School 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.
Hua Xu, Yale University Large Language Models For Biomedical Applications
Phyllis Thangaraj, Yale School of Medicine
Noémie Elhadad, Columbia University
Dhruva Biswas, Yale School of Medicine - -
Pranav Rajpurkar, Harvard Medical School 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.
Hua Xu, Yale University Large Language Models For Biomedical Applications
Phyllis Thangaraj, Yale School of Medicine
Noémie Elhadad, Columbia University
Dhruva Biswas, Yale School of Medicine - -
Location Name
Kline14 Room 1413/AB
Full Address
Kline Tower - 14th Floor
219 Prospect St
New Haven, CT 06511
United States
219 Prospect St
New Haven, CT 06511
United States
Session Type
Break