The Application of Artificial Intelligence in Lung Cancer Research - Cancer Control Volume 31 - 1-4 - Abstract

Sep 21, 2026

Uploaded by Skyler

Artificial intelligence in healthcare is reshaping medical research and clinical practice, driving major progress in oncology. 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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Abstract

Plain Language Summary

Keywords

Early Detection and Diagnosis of Lung Cancer

Imaging-based detection and diagnostic accuracy

AI-supported screening and risk populations

Personalized Treatment Plans for Lung Cancer

The Application of Artiﬁcial Intelligence in Lung Cancer Research Cancer Control Volume 31: 1–4 © The Author(s) 2024 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions) [DOI: 10.1177/10732748241297373](DOI: 10.1177/10732748241297373)[ ](DOI: 10.1177/10732748241297373)[journals.sagepub.com/home/ccx](journals.sagepub.com/home/ccx) Fang Lei, PhD, MPH, MSN, RN 1 􀀁 Abstract The advent of artiﬁcial intelligence in healthcare is transforming medical research and clinical practice, with signiﬁcant advancements in the areas of oncology. This commentary explores the pivotal role artiﬁcial intelligence plays in lung cancer research, offering insights into its current applications and future potential. Plain Language Summary Artiﬁcial 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. Keywords artiﬁcial intelligence, diagnosis, education, lung cancer, medical, research, treatment Received August 28, 2024. Received revised October 1, 2024. Accepted for publication October 14, 2024. In 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. Early Detection and Diagnosis of Lung Cancer Early detection is crucial in improving survival rates for patients with lung cancer. Traditional diagnostic methods, including 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. The 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 1 School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN, USA Corresponding Author: Fang Lei, School of Nursing, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA. Email: ﬂ[ei@umn.edu](ei@umn.edu) Data Availability Statement included at the end of the article Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-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, reproduction 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)). processing images (detection, classiﬁcation, prediction, and prognosis) of lung cancer, convolutional neural networks (CNN), generative adversarial network (GAN), K-nearest neighbor

FAQs

How does AI support early detection and diagnosis of lung cancer?

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.

What imaging tasks and models are mentioned for lung cancer research?

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).

How can AI contribute to lung cancer screening in high-risk populations?

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.

Sep 21, 2026

Uploaded by Skyler