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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] [Cited by in CrossRef: 14] [Cited by in F6Publishing: 13] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Ludwig M, Ludwig B, Mikuła A, Biernat S, Rudnicki J, Kaliszewski K. The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update. Cancers 2023;15:708. [DOI: 10.3390/cancers15030708] [Reference Citation Analysis]
2 Kamada Y, Nakamura T, Isobe S, Hosono K, Suama Y, Ohtakaki Y, Nauchi A, Yasuda N, Mitsuta S, Miura K, Yamamoto T, Hosono T, Yoshida A, Kawanishi I, Fukushima H, Kinoshita M, Umeda A, Kinoshita Y, Fukami K, Miyawaki T, Fujii H, Yoshida Y, Kawanaka M, Hyogo H, Morishita A, Hayashi H, Tobita H, Tomita K, Ikegami T, Takahashi H, Yoneda M, Jun DW, Sumida Y, Okanoue T, Nakajima A; JANIT Forum. SWOT analysis of noninvasive tests for diagnosing NAFLD with severe fibrosis: an expert review by the JANIT Forum. J Gastroenterol 2023;58:79-97. [PMID: 36469127 DOI: 10.1007/s00535-022-01932-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3(5): 117-141 [DOI: 10.35712/aig.v3.i5.117] [Reference Citation Analysis]
4 Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine. Front Pharmacol 2022;13:1079566. [PMID: 36569318 DOI: 10.3389/fphar.2022.1079566] [Reference Citation Analysis]
5 Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28(44): 6230-6248 [DOI: 10.3748/wjg.v28.i44.6230] [Reference Citation Analysis]
6 Volovat S, Augustin I, Zob D, Boboc D, Amurariti F, Volovat C, Stefanescu C, Stolniceanu CR, Ciocoiu M, Dumitras EA, Danciu M, Apostol DGC, Drug V, Shurbaji SA, Coca L, Leon F, Iftene A, Herghelegiu P. Use of Personalized Biomarkers in Metastatic Colorectal Cancer and the Impact of AI. Cancers 2022;14:4834. [DOI: 10.3390/cancers14194834] [Reference Citation Analysis]
7 Jergens AE, Heilmann RM. Canine chronic enteropathy—Current state-of-the-art and emerging concepts. Front Vet Sci 2022;9. [DOI: 10.3389/fvets.2022.923013] [Reference Citation Analysis]
8 Lewis JH. Digitizing DILI: Who can? RUCAM? RECAM? Hepatology 2022;76:3-5. [PMID: 34990036 DOI: 10.1002/hep.32312] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Tonini V, Vigutto G, Donati R. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence. Artif Intell Gastroenterol 2022; 3(2): 28-35 [DOI: 10.35712/aig.v3.i2.28] [Reference Citation Analysis]
10 Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3(1): 21-27 [DOI: 10.35712/aig.v3.i1.21] [Reference Citation Analysis]
11 Christou CD, Athanasiadou EC, Tooulias AI, Tzamalis A, Tsoulfas G. The process of estimating the cost of surgery: Providing a practical framework for surgeons. Int J Health Plann Manage 2022. [PMID: 35191067 DOI: 10.1002/hpm.3431] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
12 Huang J, Zhao C, Zhang X, Zhao Q, Zhang Y, Chen L, Dai G. A GSVA based gene set synergizing with CD4+T cell bearing harmful factors yield risk signals in HBV related diseases via amalgamation of artificial intelligence.. [DOI: 10.1101/2022.01.19.476726] [Reference Citation Analysis]
13 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]