Siegel RL, Miller KD, Wagle NS, Jemal A, Cancer Statistics. 2023. CA: A Cancer Journal for Clinicians 2023, 73 (1), 17–48. https://doi.org/10.3322/caac.21763
Du R-C, Ouyang Y-B, Hu Y. Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022. World J Gastroenterol. 2023;29(22):3561–73. https://doi.org/10.3748/wjg.v29.i22.3561.
Gavric A, Hanzel J, Zagar T, Zadnik V, Plut S, Stabuc B. Survival outcomes and rate of missed upper Gastrointestinal cancers at routine endoscopy: A single centre retrospective cohort study. Eur J Gastroenterol Hepatol. 2020;32(10):1312. https://doi.org/10.1097/MEG.0000000000001863.
Pimenta-Melo AR, Monteiro-Soares M, Libânio D, Dinis-Ribeiro M. Missing rate for gastric Cancer during upper Gastrointestinal endoscopy: A systematic review and Meta-Analysis. Eur J Gastroenterol Hepatol. 2016;28(9):1041. https://doi.org/10.1097/MEG.0000000000000657.
Xu H, Tang RSY, Lam TYT, Zhao G, Lau JYW, Liu Y, Wu Q, Rong L, Xu W, Li X, Wong SH, Cai S, Wang J, Liu G, Ma T, Liang X, Mak JWY, Xu H, Yuan P, Cao T, Li F, Ye Z, Shutian Z, Sung JJY. Artificial Intelligence–Assisted colonoscopy for colorectal Cancer screening: A multicenter randomized controlled trial. Clin Gastroenterol Hepatol. 2023;21(2):337–e3463. https://doi.org/10.1016/j.cgh.2022.07.006.
Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, Guo L, Meng Q, Yang F, Qian W, Xu Z, Wang Y, Wang Z, Gu L, Wang R, Jia F, Yao J, Li Z, Bai Y, Magnitude. Risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: A systematic review and Meta-Analysis. Gastroenterology. 2019;156(6):1661–e167411. https://doi.org/10.1053/j.gastro.2019.01.260.
Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal Cancer and death. N Engl J Med. 2014;370(14):1298–306. https://doi.org/10.1056/NEJMoa1309086.
Fockens K, Schoon EJ, Curvers W, With P. H. M. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut. 2020;69(11):2035–45. https://doi.org/10.1136/gutjnl-2019-320466. MoriY.deByrne, M.; Bergman, J. J. G.
Alagappan M, Brown JRG, Mori Y, Berzin TM. Artificial intelligence in Gastrointestinal endoscopy: the future is almost Here. World J Gastrointest Endoscopy. 2018;10(10):239–49. https://doi.org/10.4253/wjge.v10.i10.239.
Rattan P, Penrice DD, Simonetto DA. Artificial intelligence and machine learning: what you always wanted to know but were afraid to ask. Gastro Hep Adv. 2022;1(1):70–8. https://doi.org/10.1016/j.gastha.2021.11.001.
Gubatan J, Levitte S, Patel A, Balabanis T, Wei MT, Sinha SR. Artificial intelligence applications in inflammatory bowel disease: emerging technologies and future directions. World J Gastroenterol. 2021;27(17):1920–35. https://doi.org/10.3748/wjg.v27.i17.1920.
Meskó B, Görög MA. Short guide for medical professionals in the era of artificial intelligence. Npj Digit Med. 2020;3(1):1–8. https://doi.org/10.1038/s41746-020-00333-z.
Shahab O, El Kurdi B, Shaukat A, Nadkarni G, Soroush A. Large Language models: A primer and gastroenterology applications. Th Adv Gastroenterol. 2024;17:17562848241227032. https://doi.org/10.1177/17562848241227031.
Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large Language models in medicine. Nat Med. 2023;29(8):1930–40. https://doi.org/10.1038/s41591-023-02448-8.
Gong EJ, Bang CS. Revolutionizing Gastrointestinal endoscopy: the emerging role of large Language models. Clin Endosc. 2024;57(6):759–62. https://doi.org/10.5946/ce.2024.039.
Tu J, Lin X, Ye H, Yang S, Deng L, Zhu R, Wu L, Zhang X. Global Research Trends of Artificial Intelligence Applied in Esophageal Carcinoma: A Bibliometric Analysis (2000–2022) via CiteSpace and VOSviewer. Frontiers in Oncology 2022, 12.
Antonelli G. Current and future implications of artificial intelligence in colonoscopy. Aog. 2023. https://doi.org/10.20524/Aog.2023.0781.
Pace F, Buscema M, Dominici P, Intraligi M, Baldi F, Cestari R, Passaretti S, Porro GB, Grossi E. Artificial neural networks can recognize Gastro-Oesophageal reflux disease patients solely on the basis of clinical data. Eur J Gastroenterol Hepatol. 2005;17(6):605.
Lahner E, Grossi E, Intraligi M, Buscema M, Corleto VD, Fave GD, Annibale B. Possible contribution of advanced statistical methods (Artificial neural networks and linear discriminant Analysis) in recognition of patients with suspected atrophic body gastritis. World J Gastroenterol. 2005;11(37):5867–73. https://doi.org/10.3748/wjg.v11.i37.5867.
Zhang Y, Li F, Yuan F, Zhang K, Huo L, Dong Z, Lang Y, Zhang Y, Wang M, Gao Z, Qin Z, Shen L. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig Liver Disease. 2020;52(5):566–72. https://doi.org/10.1016/j.dld.2019.12.146.
Abadir AP, Ali MF, Karnes W, Samarasena JB. Artificial intelligence in Gastrointestinal endoscopy. Clin Endosc. 2020;53(2):132–41. https://doi.org/10.5946/ce.2020.038.
Briganti G, Le Moine O. Artificial intelligence in medicine: today and tomorrow. Front Med. 2020;7. https://doi.org/10.3389/fmed.2020.00027.
Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T. Application of artificial intelligence using a convolutional neural network for detecting gastric Cancer in endoscopic images. Gastric Cancer. 2018;21(4):653–60. https://doi.org/10.1007/s10120-018-0793-2.
Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in Gastrointestinal endoscopy: European society of Gastrointestinal endoscopy (ESGE) position statement. Endoscopy. 2022;54(12):1211–31. https://doi.org/10.1055/a-1950-5694.
Das A, Ben-Menachem T, Cooper GS, Chak A, Sivak MV, Gonet JA, Wong RC. Prediction of outcome in acute Lower-Gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet. 2003;362(9392):1261–6. https://doi.org/10.1016/S0140-6736(03)14568-0.
Chin S-E, Wan F-T, Ladlad J, Chue K-M, Teo E-K, Lin C-L, Foo F-J, Koh FH. (AI)-Aided endoscopy performance. Surg Endosc. 2023;37(8):6402–7. https://doi.org/10.1007/s00464-023-09979-8. SKH Endoscopy Centre. One-Year Review of Real-Time Artificial Intelligence.
Nehme F, Coronel E, Barringer DA, Romero LG, Shafi MA, Ross WA, Ge PS. Performance and attitudes toward Real-Time Computer-Aided polyp detection during colonoscopy in a large tertiary referral center in the united States. Gastrointest Endosc. 2023;98(1):100–e1096. https://doi.org/10.1016/j.gie.2023.02.016.
Richter R, Bruns J, Obst W, Keitel-Anselmino V, Weigt J. Influence of artificial intelligence on the adenoma detection rate throughout the day. Dig Dis. 2023;41(4):615–9. https://doi.org/10.1159/000528163.
Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, Taveira F, Spadaccini M, Antonelli G, Ebigbo A, Kudo S, Arribas J, Barua I, Kaminski MF, Messmann H, Rex DK, Dinis-Ribeiro M, Hassan C. Cost-Effectiveness of artificial intelligence for screening colonoscopy: A modelling study. Lancet Digit Health. 2022;4(6):e436–44. https://doi.org/10.1016/S2589-7500(22)00042-5.
Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The ethical, legal and social implications of using artificial intelligence systems in breast Cancer care. Breast. 2020;49:25–32. https://doi.org/10.1016/j.breast.2019.10.001.
Charitidis CA, Golnas A, Chouliaras F, Arpatzanis N, Dimitriadis CA, Lee JI, Bakolias CQD. Technology and market prospects in the sectors of space exploration, biomedicine, defense, and security. Phys Status Solidi C. 2008;5(12):3872–6. https://doi.org/10.1002/pssc.200780123.
Technology Forecasting of Unmanned Aerial Vehicle Technologies through Hierarchical S Curves - ProQuest. https://www.proquest.com/openview/f23d69807c34ae8754f46bedd8a0b46c/1?pq-origsite=gscholar&cbl=2028808 (accessed 2024-08-14).
Liu C-Y, Wang J-C. Forecasting the development of the biped robot walking technique in Japan through S-Curve model analysis. Scientometrics. 2010;82(1):21–36. https://doi.org/10.1007/s11192-009-0055-5.
Based on the Patent Index Method and S Curve Method Prediction Analysis of Pure Electric Vehicle Life Cycle - IOPscience. https://iopscience.iop.org/article/10.1088/1742-6596/1533/2/022105 (accessed 2024-08-14).
Patent applications as source for measuring technological performance| Scientometrics. https://link.springer.com/article/10.1007/s11192-013-1050-4 (accessed 2024-08-14).
Weinstein RS, The S-C, Framework. Predicting the future of anatomic pathology. Arch Pathol Lab Med. 2008;132(5):739–42. https://doi.org/10.5858/2008-132-739-TSFPTF.
Sheikh NJ, Sheikh O. Bibliometrics and Patents: Case of Forecasting Biosensor Technologies for Emerging Point-of-Care and Medical IoT Applications. In Innovation Discovery; Series on Technology Management; WORLD SCIENTIFIC (EUROPE), 2017; Vol. Volume 30, pp 25–43. https://doi.org/10.1142/9781786344069_0002
Chen H, Zhang G, Zhu D, Lu J. Topic-Based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014. Technol Forecast Soc Chang. 2017;119:39–52. https://doi.org/10.1016/j.techfore.2017.03.009.
Adamuthe AC, Thampi GT, Technology Forecasting. A case study of computational technologies. Technol Forecast Soc Chang. 2019;143:181–9. https://doi.org/10.1016/j.techfore.2019.03.002.
Kurniawan A, Kurniawan F Time Series Forecasting for the Spread of Covid-19 in Indonesia Using Curve Fitting. In. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT); IEEE: Surabaya, Indonesia, 2021; pp 45–48. https://doi.org/10.1109/EIConCIT50028.2021.9431936
Kucharavy D, De Guio R. Application of S-Shaped, curves. Procedia Eng. 2011;9:559–72. https://doi.org/10.1016/j.proeng.2011.03.142.
Comments (0)