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This commentary examines how AI is applied to lung cancer research today and what it is likely to enable next. Emphasis is placed on early detection and diagnosis, including image analysis from CT scans and chest X-rays, to improve accuracy, efficiency, and patient outcomes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-application-of-artificial-intelligence-in-lung-cancer-research-cancer-control-volume-31-1-4-abstract/329280/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-application-of-artificial-intelligence-in-lung-cancer-research-cancer-control-volume-31-1-4-abstract/329280.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does AI support early detection and diagnosis of lung cancer?","Question",{"text":112,"@type":113},"AI systems analyze medical images such as CT scans and chest X-rays using machine learning and deep learning to improve accuracy and efficiency. They help identify subtle patterns and nodules, supporting earlier detection and better prognosis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What imaging tasks and models are mentioned for lung cancer research?",{"text":117,"@type":113},"The text references image processing tasks including detection, classification, prediction, and prognosis. It also lists model approaches such as convolutional neural networks (CNN), generative adversarial networks (GAN), KNN, DNN, SVM, and random forest (RF).",{"name":119,"@type":110,"acceptedAnswer":120},"How can AI contribute to lung cancer screening in high-risk populations?",{"text":121,"@type":113},"AI algorithms can integrate CT imaging data to identify lung cancer before symptoms appear. 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This commentary explores the pivotal role artiﬁcial intelligence plays in lung cancer research, offering insights into its current applications and future potential.  \nPlain Language Summary  \nArtiﬁcial intelligence is changing the medical research and clinical practice, with signiﬁcant advancements in the areas of cancer. This commentary explores the important role artiﬁcial intelligence plays in lung cancer research, offering insights into its current applications and future potential.  \nKeywords  \nartiﬁcial intelligence, diagnosis, education, lung cancer, medical, research, treatment  \nReceived August 28, 2024. Received revised October 1, 2024. Accepted for publication October 14, 2024.  \nIn recent years, artiﬁcial intelligence (AI) has emerged as a transformative force across various sectors, and the ﬁeld of healthcare is no exception. The advent of AI in healthcare is transforming medical research and clinical practice, with signiﬁcant advancements in the areas of oncology. Lung cancer, one of the most prevalent and lethal cancers worldwide, is beneﬁting from AI-driven innovations that enhance early detection, personalize treatment, and improve patient outcomes. During the past 20 years, lung cancer research utilizing AI has been growing signiﬁcantly, with 2931 research studies published and 15,848 authors from 92 countries/regions involved.1 This commentary explores the pivotal role AI plays in lung cancer research, offering insights into its current applications and future potential.  \nEarly Detection and Diagnosis of Lung Cancer  \nEarly detection is crucial in improving survival rates for patients with lung cancer. Traditional diagnostic methods,  \nincluding imaging and biopsies, are often time-consuming and dependent on the expertise of radiologists. AI technologies, particularly machine learning algorithms, are enhancing the accuracy and efﬁciency oflung cancer detection.2 AI systems can analyze medical images, such as CT scans and chest X-rays, with remarkable precision.  \nThe role of AI in enhancing diagnostic accuracy of lung cancer cannot be overlooked. Diagnostic errors can have serious consequences, leading to incorrect treatments or delayed interventions. AI technologies, particularly those leveraging deep learning, have shown impressive capabilities in analyzing medical images. Depending on the purpose of  \n1 School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN, USA  \nCorresponding Author:  \nFang Lei, School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA.  \nEmail: ﬂ[ei@umn.edu](ei@umn.edu)  \nData Availability Statement included at the end of the article  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons  \nAttribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use,  \nreproduction and distribution of the work without further permission provided the original work is attributed as speciﬁed on the SAGE and Open Access pages ([https://us.sagepub.com/en-us/nam/open-access-at-sage](https://us.sagepub.com/en-us/nam/open-access-at-sage)).  \nprocessing images (detection, classiﬁcation, prediction, and prognosis) of lung cancer, convolutional neural networks (CNN), generative adversarial network (GAN), K-nearest neighbor ","cbCaifSyq1JGeU1r","https://ap.wps.com/l/cbCaifSyq1JGeU1r","pdf",525112,"English","# Abstract\n# Plain Language Summary\n# Keywords\n# Early Detection and Diagnosis of Lung Cancer\n## Imaging-based detection and diagnostic accuracy\n## AI-supported screening and risk populations\n# Personalized Treatment Plans for Lung Cancer","[{\"question\":\"How does AI support early detection and diagnosis of lung cancer?\",\"answer\":\"AI systems analyze medical images such as CT scans and chest X-rays using machine learning and deep learning to improve accuracy and efficiency. They help identify subtle patterns and nodules, supporting earlier detection and better prognosis.\"},{\"question\":\"What imaging tasks and models are mentioned for lung cancer research?\",\"answer\":\"The text references image processing tasks including detection, classification, prediction, and prognosis. It also lists model approaches such as convolutional neural networks (CNN), generative adversarial networks (GAN), KNN, DNN, SVM, and random forest (RF).\"},{\"question\":\"How can AI contribute to lung cancer screening in high-risk populations?\",\"answer\":\"AI algorithms can integrate CT imaging data to identify lung cancer before symptoms appear. The commentary also notes that comparable accuracy to radiologists can reduce workload and extend screening support for underserved areas.\"}]","The Application of Artificial Intelligence in Lung Cancer Research - Cancer Control Volume 31 - 1-4 - Abstract | PDF",1789983409]