Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice

Gabriela X. Ortiz, Ana Helena Dias Pereira dos Santos Ulbrich, Gabriele Lenhart, Henrique Dias Pereira dos Santos, Karin Hepp Schwambach, Matheus William Becker, C. Blatt
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引用次数: 0

Abstract

The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
药物性肝损伤与COVID-19:人工智能和更新的Roussel - Uclaf因果关系评估方法在临床中的应用
人工智能(AI)在胃肠道内窥镜检查中的应用在过去十年中获得了显著的关注。人工智能在该领域的最新应用之一包括检测巴雷特食管(BE)的发育不良和癌症。使用深度学习方法的人工智能有望成为内窥镜医生检测不典型增生和癌症的辅助工具。除了视觉检测和诊断之外,人工智能还可以帮助减少在识别和区分整个幻灯片图像与数字化BE组织学幻灯片上的异常增生时观察者之间的差异。本综述旨在对迄今为止的关键研究进行全面总结,并对AI在Barrett食管中的未来作用提供见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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