Expert consensus on the application of artificial intelligence in lung cancer screening, diagnosis, and treatment (2026 edition)

Wenzhao ZHONG; Haibo WANG; Yi HU; Hao ZHANG; Jigang DAI; Junqiang FAN; Guibin QIAO; Fan YANG; Jian HU; Fengwei TAN; Xuening YANG; Qiang PU; Zihao CHEN; Hongxia TIAN; Lunxu LIU; Hecheng LI; Xiaolong YAN; Zongyang YU; Zhenbin QIU; Yihua SUN; Jing HU; Yuhang SHI; Zhifei GUO; Peng ZHANG; Kezhong CHEN; Shugeng GAO; Yilong WU

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Expert consensus on the application of artificial intelligence in lung cancer screening, diagnosis, and treatment (2026 edition)

VernacularTitle:人工智能应用于肺癌筛诊疗的专家共识（2026版）
Author: Wenzhao ZHONG ¹ ; Haibo WANG ² ; Yi HU ³ ; Hao ZHANG ⁴ ; Jigang DAI ⁵ ; Junqiang FAN ⁶ ; Guibin QIAO ⁷ ; Fan YANG ⁸ ; Jian HU ⁹ ; Fengwei TAN ¹⁰ ; Xuening YANG ¹ ; Qiang PU ¹¹ ; Zihao CHEN ¹ ; Hongxia TIAN ¹ ; Lunxu LIU ¹¹ ; Hecheng LI ¹² ; Xiaolong YAN ¹³ ; Zongyang YU ¹⁴ ; Zhenbin QIU ¹ ; Yihua SUN ¹⁵ ; Jing HU ¹⁶ ; Yuhang SHI ¹⁷ ; Zhifei GUO ¹⁸ ; Peng ZHANG ¹⁹ ; Kezhong CHEN ⁸ ; Shugeng GAO ¹⁰ ; Yilong WU ¹
Author Information

1. Department of Pulmonary Surgery, Guangdong Provincial People's Hospital, Guangdong Lung Cancer Institute (Guangdong Provincial Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, P. R. China
2. Research Centre of Big Data and Artificial Intelligence in Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, P. R. China
3. Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, 100039, P. R. China
4. Department of Thoracic Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, P. R. China
5. Department of Thoracic Surgery, The Second Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, 630037, P. R. China
6. Department of Thoracic Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, P. R. China
7. Department of Thoracic Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, P. R. China
8. Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, P. R. China
9. Department of Thoracic Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310003, P. R. China
10. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, P. R. China
11. Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, 610041, P. R. China
12. Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China
13. Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, 710038, P. R. China
14. Department of Respiratory Medicine, The 900th Hospital of Joint Logistics Support Force, PLA, Fuzhou, 350000, P. R. China
15. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, P. R. China
16. Shukun Technology Company Limited, Beijing, 100039, P. R. China
17. Shanghai United Imaging Hi-Tech Research Institute Co., Ltd. Shanghai, 200025, P. R.China
18. Guangdong OptoMedic Technologies, Inc, Foshan, 528000, Guangdong, P. R. China
19. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Shanghai, 200433, P. R. China
Collective Name:on behalf of the Thoracic Surgery Branch of Chinese Medical Doctor Association, the Non-small Cell Lung Cancer Committee of Chinese Anti-Cancer Association, the Thoracic Surgery Committee of Chinese Research Hospital Association, the Pulmonary Oncology Branch of Guangdong Medical Association, and the Research Group of Noncommunicable Chronic Diseases-National Science and Technology Major Project (Development and Clinical Research of AI-based Precise Identification Technology for Pulmonary Nodules)
Publication Type:Journal Article
Keywords: Artificial intelligence; lung cancer; screening; diagnosis; treatment; deep learning; precision medicine; radiomics; multimodal data
From: Chinese Journal of Clinical Thoracic and Cardiovascular Surgery 2026;33(06):848-856
CountryChina
Language:Chinese
Abstract: With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.