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For: Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191]
URL: https://www.wjgnet.com/1007-9327/full/v27/i37/6191.htm
Number Citing Articles
1
Chrysanthos D. Christou, Eleni C. Athanasiadou, Andreas I. Tooulias, Argyrios Tzamalis, Georgios Tsoulfas. The process of estimating the cost of surgery: Providing a practical framework for surgeonsThe International Journal of Health Planning and Management 2022; 37(4): 1926 doi: 10.1002/hpm.3431
2
Kevin J. McDonnell. Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology EnterpriseJournal of Clinical Medicine 2023; 12(14): 4830 doi: 10.3390/jcm12144830
3
Marietta Iacucci, Giovanni Santacroce, Irene Zammarchi, Yasuharu Maeda, Rocío Del Amor, Pablo Meseguer, Bisi Bode Kolawole, Ujwala Chaudhari, Antonio Di Sabatino, Silvio Danese, Yuichi Mori, Enrico Grisan, Valery Naranjo, Subrata Ghosh. Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel diseaseThe Lancet Gastroenterology & Hepatology 2024; 9(8): 758 doi: 10.1016/S2468-1253(24)00053-0
4
Maksymilian Ludwig, Bartłomiej Ludwig, Agnieszka Mikuła, Szymon Biernat, Jerzy Rudnicki, Krzysztof Kaliszewski. The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An UpdateCancers 2023; 15(3): 708 doi: 10.3390/cancers15030708
5
Jovita Relasha Lewis, Sameena Pathan, Preetham Kumar, Cifha Crecil Dias. AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning TechniquesIEEE Access 2024; 12: 163764 doi: 10.1109/ACCESS.2024.3483432
6
Chrysanthos D. Christou, Angelos C. Mitsas, Ioannis Vlachavas, Georgios Tsoulfas. The Use of Machine Learning in MicroRNA Diagnostics: Current PerspectivesMicroRNA 2022; 11(3): 175 doi: 10.2174/2211536611666220818145553
7
Simona-Ruxandra Volovat, Iolanda Augustin, Daniela Zob, Diana Boboc, Florin Amurariti, Constantin Volovat, Cipriana Stefanescu, Cati Raluca Stolniceanu, Manuela Ciocoiu, Eduard Alexandru Dumitras, Mihai Danciu, Delia Gabriela Ciobanu Apostol, Vasile Drug, Sinziana Al Shurbaji, Lucia-Georgiana Coca, Florin Leon, Adrian Iftene, Paul-Corneliu Herghelegiu. Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AICancers 2022; 14(19): 4834 doi: 10.3390/cancers14194834
8
J Alfredo Martínez, Marta Alonso-Bernáldez, Diego Martínez-Urbistondo, Juan A Vargas-Nuñez, Ana Ramírez de Molina, Alberto Dávalos, Omar Ramos-Lopez. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseasesWorld Journal of Gastroenterology 2022; 28(44): 6230-6248 doi: 10.3748/wjg.v28.i44.6230
9
Valeria Tonini, Gabriele Vigutto, Riccardo Donati. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligenceArtificial Intelligence in Gastroenterology 2022; 3(2): 28-35 doi: 10.35712/aig.v3.i2.28
10
Abdulqadir J. Nashwan, Ahmad A. Abujaber, Hassan Choudry. Embracing the future of physician-patient communication: GPT-4 in gastroenterologyGastroenterology & Endoscopy 2023; 1(3): 132 doi: 10.1016/j.gande.2023.07.004
11
Hong-Niu Wang, Jia-Hao An, Liang Zong. Estimating prognosis of gastric neuroendocrine neoplasms using machine learning: A step towards precision medicineWorld Journal of Gastrointestinal Oncology 2024; 16(12): 4548-4552 doi: 10.4251/wjgo.v16.i12.4548
12
Zoltan Czako, Teodora Surdea-Blaga, Gheorghe Sebestyen, Anca Hangan, Dan Lucian Dumitrascu, Liliana David, Giuseppe Chiarioni, Edoardo Savarino, Stefan Lucian Popa. Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal Manometry Using Machine LearningSensors 2021; 22(1): 253 doi: 10.3390/s22010253
13
Albert E. Jergens, Romy M. Heilmann. Canine chronic enteropathy—Current state-of-the-art and emerging conceptsFrontiers in Veterinary Science 2022; 9 doi: 10.3389/fvets.2022.923013
14
Faizan Siddiqui, Danish Aslam, Khushnuma Tanveer, Mohamed Soudy. Artificial Intelligence and Autoimmune DiseasesStudies in Computational Intelligence 2024; 1133: 61 doi: 10.1007/978-981-99-9029-0_3
15
Abhimati Ravikulan, Kamran Rostami. Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicineWorld Journal of Gastroenterology 2024; 30(5): 424-428 doi: 10.3748/wjg.v30.i5.424
16
Jonathan S Galati, Robert J Duve, Matthew O'Mara, Seth A Gross. Artificial intelligence in gastroenterology: A narrative reviewArtificial Intelligence in Gastroenterology 2022; 3(5): 117-141 doi: 10.35712/aig.v3.i5.117
17
Chrysanthos D Christou, Georgios Tsoulfas. Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!World Journal of Gastroenterology 2023; 29(48): 6168-6178 doi: 10.3748/wjg.v29.i48.6168
18
Zahra Akbari Heydarabadi, Seyedeh Somayeh Naghibi. Diagnosing Thyroid-associated Ophthalmopathy with AI Algorithms Based on Facial Images: a Review2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP) 2024; : 1 doi: 10.1109/AISP61396.2024.10475288
19
Pierre Allaume, Noémie Rabilloud, Bruno Turlin, Edouard Bardou-Jacquet, Olivier Loréal, Julien Calderaro, Zine-Eddine Khene, Oscar Acosta, Renaud De Crevoisier, Nathalie Rioux-Leclercq, Thierry Pecot, Solène-Florence Kammerer-Jacquet. Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic ReviewDiagnostics 2023; 13(10): 1799 doi: 10.3390/diagnostics13101799
20
Marcos Mucenic, Ajacio Bandeira de Mello Brandão, Claudio Augusto Marroni. Artificial intelligence and human liver allocation: Potential benefits and ethical implicationsArtificial Intelligence in Gastroenterology 2022; 3(1): 21-27 doi: 10.35712/aig.v3.i1.21
21
Jun Huang, Chunbei Zhao, Xinhe Zhang, Qiaohui Zhao, Yanting Zhang, Liping Chen, Guifu Dai. Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicineFrontiers in Pharmacology 2022; 13 doi: 10.3389/fphar.2022.1079566
22
Yoshihiro Kamada, Takahiro Nakamura, Satoko Isobe, Kumiko Hosono, Yukiko Suama, Yukie Ohtakaki, Arihito Nauchi, Naoto Yasuda, Soh Mitsuta, Kouichi Miura, Takuma Yamamoto, Tatsunori Hosono, Akihiro Yoshida, Ippei Kawanishi, Hideaki Fukushima, Masao Kinoshita, Atsushi Umeda, Yuichi Kinoshita, Kana Fukami, Toshio Miyawaki, Hideki Fujii, Yuichi Yoshida, Miwa Kawanaka, Hideyuki Hyogo, Asahiro Morishita, Hideki Hayashi, Hiroshi Tobita, Kengo Tomita, Tadashi Ikegami, Hirokazu Takahashi, Masato Yoneda, Dae Won Jun, Yoshio Sumida, Takeshi Okanoue, Atsushi Nakajima. SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT ForumJournal of Gastroenterology 2023; 58(2): 79 doi: 10.1007/s00535-022-01932-1
23
James H. Lewis. Digitizing DILI: Who can? RUCAM? RECAM?Hepatology 2022; 76(1): 3 doi: 10.1002/hep.32312
24
Nina DeFranco Tommarello, Rebecca A. Deek. The Convergence of the Internet of Things and Artificial Intelligence in Medicine: Assessing the Benefits, Challenges, and RisksComputer 2024; 57(2): 95 doi: 10.1109/MC.2023.3321188