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A case study on an AI pipeline to review charts

Artificial intelligence (AI) overall, and natural language processing (NLP) in particular, have been shown to aid in patient chart summarization.[1] The academic literature on AI is constantly exposing new uses for the technology for understanding medical records, and using that data to automate tasks and assist health-care workers.[2] AI’s presence in the popular press is even more astounding. This includes articles written about AI and, more astonishingly, articles written by AI models.[3] In almost all of these contexts, AI is often described as a monolithic technology. It is portrayed as a magical “black box,” where raw data flows in and useful insights flow out. The technology is referred to as “the AI,” “the model,” or “the platform,” or even anthropomorphized as “he,” “she,” Siri, Alexa,® or ChatGPT.® Although convenient, and useful for marketing, such a presentation of the technology has drawbacks. It makes it harder for AI consumers to understand what the technology is doing, identify what solutions are best for specific tasks, and determine how AI can best be integrated into existing uses, cases, and workflows. More generally, it mystifies the technology into something exciting yet intimidating. In this paper, we will pull back the curtain on a clinical AI platform, show its component parts, as an illustrative example of how the technology works.

Many commercial grade AI solutions are not one single model, but rather a series of modeling technologies working in concert. The work of “being intelligent” is broken down into a set of rudimentary precursor tasks, often performed sequentially to create what is commonly called an AI pipeline. Understanding these discrete tasks is the key to understanding how AI works.


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