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For: Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25(14): 1666-1683 [PMID: 31011253 DOI: 10.3748/wjg.v25.i14.1666] [Cited by in CrossRef: 141] [Cited by in F6Publishing: 144] [Article Influence: 35.3] [Reference Citation Analysis]
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10 Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3(4): 96-104 [DOI: 10.35712/aig.v3.i4.96] [Reference Citation Analysis]
11 Seager A, Sharp L, Hampton JS, Neilson LJ, Lee TJW, Brand A, Evans R, Vale L, Whelpton J, Rees CJ. Trial protocol for COLO-DETECT: A randomized controlled trial of lesion detection comparing colonoscopy assisted by the GI Genius™ artificial intelligence endoscopy module with standard colonoscopy. Colorectal Dis 2022;24:1227-37. [PMID: 35680613 DOI: 10.1111/codi.16219] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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14 Gong EJ, Bang CS, Lee JJ, Yang YJ, Baik GH. Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images. JPM 2022;12:1361. [DOI: 10.3390/jpm12091361] [Reference Citation Analysis]
15 Pernencar C, Saboia I, Dias JC. How Far Can Conversational Agents Contribute to IBD Patient Health Care—A Review of the Literature. Front Public Health 2022;10:862432. [DOI: 10.3389/fpubh.2022.862432] [Reference Citation Analysis]
16 Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3(3): 70-86 [DOI: 10.35711/aimi.v3.i3.70] [Reference Citation Analysis]
17 Gong EJ, Bang CS, Jung K, Kim SJ, Kim JW, Seo SI, Lee U, Maeng YB, Lee YJ, Lee JI, Baik GH, Lee JJ. Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study. JPM 2022;12:1052. [DOI: 10.3390/jpm12071052] [Reference Citation Analysis]
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19 Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel) 2022;7:79. [PMID: 35735595 DOI: 10.3390/biomimetics7020079] [Reference Citation Analysis]
20 Gong EJ, Bang CS, Lee JJ, Seo SI, Yang YJ, Baik GH, Kim JW. No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification. JPM 2022;12:963. [DOI: 10.3390/jpm12060963] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Fu XY, Mao XL, Chen YH, You NN, Song YQ, Zhang LH, Cai Y, Ye XN, Ye LP, Li SW. The Feasibility of Applying Artificial Intelligence to Gastrointestinal Endoscopy to Improve the Detection Rate of Early Gastric Cancer Screening. Front Med (Lausanne) 2022;9:886853. [PMID: 35652070 DOI: 10.3389/fmed.2022.886853] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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23 Zhang F, Wu S, Qu M, Zhou L. Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children. Contrast Media Mol Imaging 2022;2022:1013241. [PMID: 35585944 DOI: 10.1155/2022/1013241] [Reference Citation Analysis]
24 Tian L, Zhang Z, Long Y, Tang A, Deng M, Long X, Fang N, Yu X, Ruan X, Qiu J, Wang X, Deng H. Endoscopists' Acceptance on the Implementation of Artificial Intelligence in Gastrointestinal Endoscopy: Development and Case Analysis of a Scale. Front Med (Lausanne) 2022;9:760634. [PMID: 35492311 DOI: 10.3389/fmed.2022.760634] [Reference Citation Analysis]
25 Goyal H, Sherazi SAA, Gupta S, Perisetti A, Achebe I, Ali A, Tharian B, Thosani N, Sharma NR. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. Therap Adv Gastroenterol 2022;15:175628482210938. [DOI: 10.1177/17562848221093873] [Reference Citation Analysis]
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27 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]
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38 Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. Theranostics and Precision Medicine for the Management of Hepatocellular Carcinoma, Volume 2 2022. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Reference Citation Analysis]
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40 Czako Z, Surdea-Blaga T, Sebestyen G, Hangan A, Dumitrascu DL, David L, Chiarioni G, Savarino E, Popa SL. Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine Learning. Sensors (Basel) 2021;22:253. [PMID: 35009794 DOI: 10.3390/s22010253] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
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42 Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis. J Med Internet Res 2021;23:e33267. [PMID: 34904949 DOI: 10.2196/33267] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
43 Bang CS. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. Korean J Helicobacter Up Gastrointest Res 2021;21:300-10. [DOI: 10.7704/kjhugr.2021.0030] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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47 Hardy NP, Cahill RA. Digital surgery for gastroenterological diseases. World J Gastroenterol 2021; 27(42): 7240-7246 [PMID: 34876786 DOI: 10.3748/wjg.v27.i42.7240] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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51 El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2(5): 124-132 [DOI: 10.35712/aig.v2.i5.124] [Reference Citation Analysis]
52 Caliskan UK, Karakus MM. Evaluation of botanicals as potential COVID-19 symptoms terminator . World J Gastroenterol 2021; 27(39): 6551-6571 [PMID: 34754152 DOI: 10.3748/wjg.v27.i39.6551] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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60 Nsugbe E, Samuel OW, Sanusi I, Asogbon MG, Li G. A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods. IET cyber-systems robotics 2021;3:228-44. [DOI: 10.1049/csy2.12031] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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63 Yoo BS, D'Souza SM, Houston K, Patel A, Lau J, Elmahdi A, Parekh PJ, Johnson D. Artificial intelligence and colonoscopy − enhancements and improvements. Artif Intell Gastrointest Endosc 2021; 2(4): 157-167 [DOI: 10.37126/aige.v2.i4.157] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
64 Garland J, Hu M, Duffy M, Kesha K, Glenn C, Morrow P, Stables S, Ondruschka B, Da Broi U, Tse RD. Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks. Am J Forensic Med Pathol 2021;42:230-4. [PMID: 33833193 DOI: 10.1097/PAF.0000000000000672] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
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