On the Opportunities and Risks of Large Models for the Field of Medical and Health Care: Keynote Address

Abstract

A Large Model is any model that is trained on broad data that can be fine-tuned to a wide range of downstream tasks; current examples include SAM, GPT-3, GPT-4, ChatDoctor, etc.. From a technological point of view, large models are not new, they are based on deep neural networks and self-supervised learning, both of which have existed for decades. However, the sheer scale and scope of large models from the last few years have stretched our imagination of what is possible; for example, GPT-3 has 175 billion parameters and can be adapted via natural language prompts to do a passable job on a wide range of tasks despite not being trained explicitly to do many of those tasks. Large models acquire linguistic and visual capabilities to perform reasoning and search, and interact with humans. The capabilities of large models indicate that they have the potential to transform various sectors and industries, extending the role AI plays in society. At the same time, existing large models have the potential to accentuate harms, and their characteristics are in general poorly understood. In this talk, She will first overviews the emerging paradigms for building artificial intelligence (AI) systems based on a general class of models which we term large models, in the field of medical and health care. In addition, She will discuss the opportunities and challenges for the application of large models in this field.

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