Advancements in LLMs such as ChatGPT and GPT-4 have generated substantial excitement. Many see these models as assistants or even potential replacements for time-intensive tasks, like patient-physician communication through the electronic health record. Designed to serve numerous downstream applications, these models convert data into representations that are useful for multiple tasks. As a result, they have been labeled “foundation models.”
Yet a core question remains: As exciting as it is to chat with an AI tool that has read more text than you will in your lifetime, will such models in their current state really transform health care? We think the answer is no. But one approach customized for medicine could.
Largely based on established AI methodologies, the recent success of foundation models is due in large part to their massive scale. Online sources like Wikipedia, Flickr, and YouTube provide a firehose of text, images and video data for training. In June 2022, it was estimated that nearly 500 hours of video data are uploaded to YouTube every minute. The size and breadth of these corpora feed into the ability of foundation models to serve multiple downstream tasks.
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