Literature DB >> 33624891

Applications of artificial intelligence in pancreatic and biliary diseases.

Po-Ting Chen1, Dawei Chang2, Tinghui Wu2, Ming-Shiang Wu3,4, Weichung Wang2, Wei-Chih Liao3,4.   

Abstract

The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.
© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  Artificial intelligence; Biliary disease; Pancreas

Year:  2021        PMID: 33624891     DOI: 10.1111/jgh.15380

Source DB:  PubMed          Journal:  J Gastroenterol Hepatol        ISSN: 0815-9319            Impact factor:   4.029


  2 in total

1.  Effect Evaluation of Artificial Intelligence-Based Electronic Health PDCA Nursing Model in the Treatment of Mycoplasma Pneumonia in Children.

Authors:  Yan Zhao
Journal:  J Healthc Eng       Date:  2022-03-11       Impact factor: 2.682

2.  Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.

Authors:  Péter Hegyi; Andrea Szentesi; Szabolcs Kiss; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Zoltán Sipos; Péter Fehérvári; László Pecze; Mária Földi; Áron Vincze; Tamás Takács; László Czakó; Ferenc Izbéki; Adrienn Halász; Eszter Boros; József Hamvas; Márta Varga; Artautas Mickevicius; Nándor Faluhelyi; Orsolya Farkas; Szilárd Váncsa; Rita Nagy; Stefania Bunduc; Péter Jenő Hegyi; Katalin Márta; Katalin Borka; Attila Doros; Nóra Hosszúfalusi; László Zubek; Bálint Erőss; Zsolt Molnár; Andrea Párniczky
Journal:  Sci Rep       Date:  2022-05-12       Impact factor: 4.996

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.