Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/8311
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dc.contributor.authorHuang, Shigaoen_US
dc.contributor.authorYang, Jieen_US
dc.contributor.authorDr. SHEN Na, Nellen_US
dc.contributor.authorXu, Qingsongen_US
dc.contributor.authorZhao, Qien_US
dc.date.accessioned2023-10-19T03:50:30Z-
dc.date.available2023-10-19T03:50:30Z-
dc.date.issued2023-
dc.identifier.citationSeminars in Cancer Biology, 2023, Vol. 89, pp. 30-37.en_US
dc.identifier.issn1096-3650-
dc.identifier.issn1044-579X-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/8311-
dc.description.abstractLung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.en_US
dc.language.isoenen_US
dc.relation.ispartofSeminars in Cancer Biologyen_US
dc.titleArtificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspectiveen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1016/j.semcancer.2023.01.006-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Business Administration-
Appears in Collections:Business Administration - Publication
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