Post written by Tessa Herman, MD, from the Department of Gastroenterology, Hepatology, and Nutrition, Vanderbilt University Medical Center, Nashville, Tennessee, and Daniela Guerrero Vinsard, MD, from the Gastroenterology Section, Minneapolis Veteran Affairs Medical Center, and Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA. This nationwide survey study assessed gastroenterology (GE) fellows’ exposure to and experience with artificial intelligence (AI) …
Tag: Artificial intelligence
Efficacy of artificial intelligence–assisted upper gastrointestinal endoscopy for neoplasm detection: a systematic review and meta-analysis of randomized controlled trials
Post written by Edoardo Vincenzo Savarino, MD, PhD, from the Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy, Mohammad Al Hayek, MD, from the Faculty of Medicine, Damascus University, Damascus, Syrian Arab Republic, and Brigida Barberio, MD, PhD, from the Gastroenterology Unit, Department of Surgery, Oncology and Gastroenterology, University of Padova. Upper GI cancers remain a …
Best of artificial intelligence in GI endoscopy
Post written by Michael B. Wallace, MD, MPH, from Mayo Clinic Florida, Jacksonville, Florida, USA. This is part of a series of invited reviews focusing on the best articles of the past year. "Best of artificial intelligence in GI endoscopy" allows our endoscopic community to get a rapid overview of the top publications in the field of artificial …
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Creating a standardized tool for the evaluation and comparison of artificial intelligence–based computer-aided detection programs in colonoscopy: a modified Delphi approach
Post written by Sanjay Gadi, MD, and Jeremy Glissen Brown, MD, from Duke University Medical Center, Durham, North Carolina, USA. Artificial intelligence—based computer-aided detection (CADe) software may improve colorectal cancer outcomes by increasing adenoma detection while reducing miss rates during colonoscopy. CADe has had promising results in controlled environments but mixed results when evaluated in …
A convolutional neural network–based system for identifying neuroendocrine neoplasms and multiple types of lesions in the pancreas using EUS (with videos)
Post written by Zhen Li, MD, from the Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China. The focus of our study was to develop a convolutional neural network—based artificial intelligence (AI) system named iEUS to assist in diagnosing pancreatic neuroendocrine neoplasms (pNENs) and multiple types of pancreatic lesions (including pancreatic adenocarcinoma, autoimmune pancreatitis, and pancreatic …
Unveiling the effectiveness of Chat-GPT 4.0, an artificial intelligence conversational tool, for addressing common patient queries in gastrointestinal endoscopy
Post written by Roberta Maselli, MD, PhD, from the Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy. This study evaluates the effectiveness and reliability of Chat Generative Pre-Trained Transformer 4.0 (Chat-GPT 4.0; OpenAI, San Francisco, Calif, USA) in addressing common patient queries regarding GI endoscopy. By assessing responses in terms of reliability, accuracy, and comprehensibility, the …
Preliminary validation of the virtual bariatric endoscopic simulator
Post written by Mark A. Gromski, MD, from the Division of Gastroenterology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, Suvranu De, ScD, from the College of Engineering, Florida A&M University-Florida State University, Tallahassee, Florida, and Doga Demirel, PhD, from the School of Computer Science, University of Oklahoma, Norman, Oklahoma, USA. This study focuses on developing and validating the virtual …
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Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis
Post written by Jonathan Makar, BSc, from The University of Melbourne, Melbourne, Victoria, Australia. Our study focuses on the impact of computer-aided detection (CADe) systems and their role in improving adenoma detection during colonoscopy. These novel artificial intelligence (AI) systems aim to address endoscopist recognition failure and improve colonoscopy performance, as they are not subject to human …
The best of artificial intelligence in 2024
Post written by Michael B. Wallace, MD, MPH, GIE Editor Emeritus from the Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA. This was an invited review article commissioned by the GIE Editorial Board to recap major advances in the field of artificial intelligence (AI) over the past year. AI is moving so rapidly in many fields, including …
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A new artificial intelligence system for both stomach and small-bowel capsule endoscopy
Post written by Xia Xie, MD, PhD, and Shi-Ming Yang, MD, PhD, from the Department of Gastroenterology, The Second Affiliated Hospital, The Third Military Medical University, Chongqing, China. This research primarily focuses on analysis of artificial intelligence (AI)’s capacity for recognizing lesions in the stomach and small intestine as well as its potential to augment the diagnostic …
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