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An important application of AI in clinical practice is the automated extraction of information from unstructured data stored in EHRs. EHRs contain information that is rich in insights into patient disease status, but that is stored in a form that is not easily machine readable. In particular, EHRs contain unstructured data, such as text notes and images, and structured data, such as laboratory results and prescriptions, that are stored in de-identified, coded formats. Standard techniques for processing structured data, such as data-mining of laboratory values and extraction of gene names and gene-gene relationships, are easy to apply even in the absence of a formal programming language for the storage of data. However, processing unstructured data, such as the extraction of note text into structured data, is a more difficult task that requires understanding of the context in which the notes were taken and understanding the underlying logic of the language used in the text. Although NLP technologies have been used for the generation of structured data from unstructured data, such as clinical narratives, they are also useful for the discovery of rules from unstructured data. This is due to their ability to extract information that is otherwise difficult for a human to identify, such as the detection of temporal patterns in patient behavior, or the identification of patients with similar disease phenotypes. In particular, NLP-based methods may be effective for the automated extraction of new phenotypes from EHRs [44].

AI in medicine also includes the use of computational tools to augment the physician’s understanding of medical images, including x-ray, computed tomography (CT), and magnetic resonance imaging (MRI) scans. Despite advances in CT and MRI hardware, the information gleaned from scans is typically limited to the visual interpretation of the image. AI tools may be able to help physicians to understand these images better, for example, by detecting which organs are damaged, even when the contours of those organs cannot be seen in a particular scan. Recently developed deep learning methods are particularly effective for this task, because they can learn how to detect features that humans cannot detect easily in the images, such as the location of damaged organs. However, deep learning methods have been shown to require a huge number of images in order to train, making them a less practical option for routine clinical use.

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