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- W4366337933 abstract "ABSTRACT Introduction The Lyon Consensus Conference proposed criteria for the clinical diagnosis of three different phenotypes of gastroesophageal reflux disease: nonerosive gastroesophageal reflux disease, Reflux Hypersensitivity, and Functional Heartburn. Methods In the present study, we examined the potential of ChatGPT, an artificial intelligence-based conversational large language model to describe how one can identify the different phenotypes as identified by the Lyon Consensus Conference, and to provide a diagnosis when given important clinical findings in a given patient with a particular phenotype. Results Although in our analyses ChatGPT captured correct information regarding symptoms, upper gastrointestinal endoscopy findings and response to gastric antisecretory agents when asked to describe different phenotypes, it failed, however, to return correct information on esophageal acid exposure time and the association of symptoms with esophageal reflux episodes. ChatGPT was even less effective in returning the correct diagnosis after being given specific clinical features of a particular phenotype. Conclusions Although it seems likely that the ability of ChatGPT to capture information from multiple sources will improve with future use and refinement, presently it is inadequate as a standalone tool for processing information for the description or diagnosis of different clinical disease states. On the other hand, artificial intelligence might prove useful to clinicians in performing tasks that involve obtaining data from a single source such as the electronic medical record and generating a document having a standardized format such as a discharge summary." @default.
- W4366337933 created "2023-04-20" @default.
- W4366337933 creator A5010478795 @default.
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- W4366337933 date "2023-04-18" @default.
- W4366337933 modified "2023-10-17" @default.
- W4366337933 title "IDENTIFYING DIFFERENT PHENOTYPES OF SYMPTOMATIC GASTROESOPHAGEAL REFLUX DISEASE USING ARTIFICIAL INTELLIGENCE" @default.
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- W4366337933 doi "https://doi.org/10.1101/2023.04.14.23288596" @default.
- W4366337933 hasPublicationYear "2023" @default.
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