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Following PRISMA-guided procedures, it screens and synthesizes evidence from 39 included studies selected from 1024 records. The analysis evaluates AI model performance, highlighting strengths and limitations, while addressing heterogeneity across study parameters. Results report pooled sensitivity and specificity of 0.87, supporting diagnostic accuracy and underscoring the need for standardized protocols and future validation for clinical adoption.",{"@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/ai-driven-models-for-diagnosing-and-predicting-outcomes-in-lung-cancer-a-systematic-review-and-meta-analysis/326880/",{"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/ai-driven-models-for-diagnosing-and-predicting-outcomes-in-lung-cancer-a-systematic-review-and-meta-analysis/326880.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-12","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main aim of the study on lung cancer?","Question",{"text":112,"@type":113},"The study evaluates AI-driven models for early detection of lung cancer and for predicting outcomes by systematically reviewing and meta-analyzing available evidence.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were studies selected and analyzed?",{"text":117,"@type":113},"The review followed PRISMA guidelines, screening 1024 identified records and selecting 39 studies that met predefined inclusion criteria, with data extraction and quality assessment.",{"name":119,"@type":110,"acceptedAnswer":120},"What diagnostic performance did the meta-analysis report?",{"text":121,"@type":113},"The pooled sensitivity and specificity were 0.87, indicating strong accuracy for identifying true positives and negatives despite heterogeneity among studies.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},326880,1790032895,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","cancers   \nSystematic Review  \nAI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis  \nMohammed Kanan 1, *, Hajar Alharbi 2, Nawaf Alotaibi 3, Lubna Almasuood 4, Shahad Aljoaid 5, Tuqa Alharbi 6, Leen Albraik 7, Wojod Alothman 8, Hadeel Aljohani 9, Aghnar Alzahrani 10, Sadeem Alqahtani 11,  \nRazan Kalantan 9, Raghad Althomali 12, Maram Alameen 12 and Ahdab Mufti 13  \nCitation: Kanan, M.; Alharbi, H.; Alotaibi, N.; Almasuood, L.; Aljoaid, S.; Alharbi, T.; Albraik, L.; Alothman, W.; Aljohani, H.; Alzahrani, A.; et al.  \nAI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis. Cancers 2024, 16, 674. [https://](https://)[ ](https://)[doi.org/10.3390/cancers16030674](doi.org/10.3390/cancers16030674)  \nAcademic Editors: Athanasia Pataka, Serafeim Chrysovalantis Kotoulas and Dionisios G. Spyratos  \nReceived: 19 November 2023  \nRevised: 20 January 2024  \nAccepted: 25 January 2024  \nPublished: 5 February 2024  \nCopyright: © 2024 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 Department of Clinical Pharmacy, King Fahad Medical City, Riyadh 12211, Saudi Arabia  \n2 Department of Medicine, Gdansk Medical University, 80210 Gdansk, Poland  \n3 Department of Clinical Pharmacy, Northern Border University, Rafha 73213, Saudi Arabia  \n4 Department of Pharmacy, Qassim University, Buraydah 52571, Saudi Arabia  \n5 Department of Medicine, University of Tabuk, Tabuk 47911, Saudi Arabia  \n6 Department of Medicine, Qassim University, Buraydah 52571, Saudi Arabia  \n7 Department of Medicine, Al-Faisal University, Riyadh 12385, Saudi Arabia; [lsalbraik@alfaisal.edu](lsalbraik@alfaisal.edu)  \n8 Department of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31411, Saudi Arabia  \n9 Department of Medicine and Surgery, King Abdulaziz University, Jeddah 22230, Saudi Arabia  \n10 Department of Medicine, Al-Baha University, Al Bahah 65964, Saudi Arabia  \n11 Department of Pharmacy, King Khalid University, Abha 62217, Saudi Arabia  \n12 Department of Medicine, Taif University, Taif 26311, Saudi Arabia  \n13 Department of Medicine, Ibn Sina National College, Jeddah 22230, Saudi Arabia  \n* [Correspondence: mkaalshammari@kfmc.med.sa](Correspondence: mkaalshammari@kfmc.med.sa)  \nSimple Summary: This research explores the transformative potential of artificial intelligence (AI) in the early detection of lung cancer. Through a comprehensive systematic review and meta-analysis, this study evaluates the effectiveness of AI models, emphasizing a promising avenue for improving diagnostic accuracy. Among 1024 identified records, 39 studies were meticulously selected and analyzed following the PRISMA guidelines. The findings highlight significant strides in AI’s role, emphasizing the need for standardized protocols. Despite the observed heterogeneity, this study underscores AI’s promising impact on lung cancer screening, laying the groundwork for future advancements in clinical practice. This research contributes crucial insights for healthcare professionalsand researchers alike, aiming to enhance the early diagnosis and management of lung cancer.  \nAbstract: (1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis follow","cbCaigZe5saom0ek","https://ap.wps.com/l/cbCaigZe5saom0ek","pdf",3057076,18,"English","# Introduction\n# Methods\n## PRISMA-guided search and selection\n# Results\n## Pooled sensitivity and specificity","[{\"question\":\"What is the main aim of the study on lung cancer?\",\"answer\":\"The study evaluates AI-driven models for early detection of lung cancer and for predicting outcomes by systematically reviewing and meta-analyzing available evidence.\"},{\"question\":\"How were studies selected and analyzed?\",\"answer\":\"The review followed PRISMA guidelines, screening 1024 identified records and selecting 39 studies that met predefined inclusion criteria, with data extraction and quality assessment.\"},{\"question\":\"What diagnostic performance did the meta-analysis report?\",\"answer\":\"The pooled sensitivity and specificity were 0.87, indicating strong accuracy for identifying true positives and negatives despite heterogeneity among studies.\"}]","AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis | PDF",1789975641,45]