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Lung cancer screening enables earlier detection and more effective therapies by identifying malignancies sooner and improving patient outcomes. 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Artificial Intelligence in Lung Cancer Screening: The Future Is Now.  \nCancers 2023, 15, 4344 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15174344  \nAcademic Editor: Hideya Yamazaki  \nReceived: 10 July 2023  \nRevised: 27 August 2023  \nAccepted: 28 August 2023  \nPublished: 30 August 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milano, Italy; [michaela.cellina@asst-fbf-sacco.it](michaela.cellina@asst-fbf-sacco.it)  \n2 Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy;  \n[laura.cacioppa@gmail.com](laura.cacioppa@gmail.com) (L.M.C.); [nicolorossini44@gmail.com](nicolorossini44@gmail.com) (N.R.); [alessandrabruno92@gmail.com](alessandrabruno92@gmail.com) (A.B.)  \n3 Division of Interventional Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy  \n4 Postgraduation School in Radiodiagnostics, Universit degli Studi di Milano, 20122 Milan, Italy; [maurizioce.md1@gmail.com](maurizioce.md1@gmail.com) (M.C.); vittoria.chiarpenello@unimi.it (V.C.); [marco.costa@unimi.it](marco.costa@unimi.it) (M.C.); [zakaria.vincenzo@unimi.it](zakaria.vincenzo@unimi.it) (Z.V.); [daniele.pais@unimi.it](daniele.pais@unimi.it) (D.P.); mariavittoria.bausano@unimi.it (M.V.B.)  \n5 Division of Radiology, Department of Radiological Sciences, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy  \n* Correspondence: chiara.fl[oridi@gmail.com](oridi@gmail.com)  \nSimple Summary: Lung cancer is a widespread malignant tumour with a high mortality and morbidity rate and is frequently diagnosed in the middle and late stages when few therapies are available. Lung cancer screening allows an early-stage diagnosis and more effective therapies. Artificial intelligence (AI) plays a key role in lung cancer screening workflow for early diagnosis. Particularly, in low-dose computed tomography for screening programs, AI further reduces radiation dose maintaining an optimal image quality. AI also allows risk stratification and subsequent screening personalization. A computer-aided detection (CAD) system helps in lung nodule detection with a high sensibility, reducing imaging time interpretation. AI is additionally applied in nodule characterization (benign or malignant), using different approaches. This narrative review aims to provide an overall view of all possible AI applications in lung cancer screening.  \nAbstract: Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients' survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients' outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in th","cbCailABlEnej0kV","https://ap.wps.com/l/cbCailABlEnej0kV","pdf",3432534,25,"English","# 1. Introduction\n## Simple Summary\n## Abstract\n## Keywords\n## Core Topics in AI for Lung Cancer Screening","[{\"question\":\"What problem does lung cancer screening aim to solve?\",\"answer\":\"It targets the frequent late-stage diagnosis of lung cancer by enabling earlier detection, when more effective treatments are available.\"},{\"question\":\"How does AI reduce radiation exposure in screening?\",\"answer\":\"In low-dose computed tomography screening programs, AI-based reconstruction can further reduce radiation dose while maintaining optimal image quality.\"},{\"question\":\"What roles can AI play after a lung nodule is detected?\",\"answer\":\"AI can characterize nodules as benign or malignant using automatic segmentation for lesion assessment and, alternatively, segmentation followed by radiomic feature extraction for a “virtual biopsy”.\"}]","Artificial Intelligence in Lung Cancer Screening - The Future Is Now | PDF",1790272911,63]