Artificial Intelligence as a Tool in Diagnosing Inflammatory Bowel Disease in Older Adults.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL Journal of Clinical Medicine Pub Date : 2025-02-18 DOI:10.3390/jcm14041360
Ana-Gabriela Prada, Tudor Stroie, Rucsandra-Ilinca Diculescu, George Cristian Gogîrlă, Codruța Delia Radu, Doina Istratescu, Gabriel Ioan Prada, Mihai Mircea Diculescu
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引用次数: 0

Abstract

Background/Objectives: The primary objective of our study was to find a potential use for images generated by imagistic investigations by comparing the appearance of a healthy digestive tract to that of a pathological one. Methods: We conducted a cross-sectional observational study involving 60 older adult patients admitted to and followed up at a primary center in Romania. Our focus was on different diagnostic methods and the use of artificial intelligence (AI) tools integrated into the electronic health records system. Results: Currently, imagery, laboratory values and electronic health records (EHR) can also be used to train AI models. Comparative imagery to predict the appearance of inflammatory bowel disease (IBD) can be used as a predictor model. Conclusions: Our findings indicate with certainty that training a tool in the diagnosis and prevention of relapses in older adults with IBD is promising for further integrating these models into patient care.

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来源期刊
Journal of Clinical Medicine
Journal of Clinical Medicine MEDICINE, GENERAL & INTERNAL-
CiteScore
5.70
自引率
7.70%
发文量
6468
审稿时长
16.32 days
期刊介绍: Journal of Clinical Medicine (ISSN 2077-0383), is an international scientific open access journal, providing a platform for advances in health care/clinical practices, the study of direct observation of patients and general medical research. This multi-disciplinary journal is aimed at a wide audience of medical researchers and healthcare professionals. Unique features of this journal: manuscripts regarding original research and ideas will be particularly welcomed.JCM also accepts reviews, communications, and short notes. There is no limit to publication length: our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible.
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