Key Takeaways & Executive Findings
- •• AI-based imaging analysis achieves high sensitivity and specificity in lung nodule detection, reducing false positives and aiding early diagnosis. • Integration of AI with genomic and pathological data enables personalized treatment recommendations, improving patient outcomes. • Natural language processing facilitates extraction of clinical information from unstructured data, enhancing research and decision support. • Despite potential, AI adoption faces barriers including data standardization, interpretability, and ethical concerns, requiring rigorous validation and governance.
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in oncology, particularly in the diagnosis and treatment of lung cancer. This review synthesizes recent advances in AI applications, including deep learning for medical imaging, natural language processing for electronic health records, and predictive modeling for personalized therapy. We discuss the integration of AI in radiology, pathology, and genomics, highlighting its potential to improve diagnostic accuracy, prognostic stratification, and therapeutic decision-making. Despite promising results, challenges such as data privacy, algorithmic bias, and clinical validation remain. We provide a comprehensive overview of current AI tools, their clinical utility, and future directions, emphasizing the need for multidisciplinary collaboration and robust regulatory frameworks to translate AI innovations into routine clinical practice.
1. Introduction
Lung cancer remains the leading cause of cancer-related mortality worldwide, with a five-year survival rate of less than 20% for advanced stages. Early detection and precise treatment are crucial for improving prognosis. Artificial intelligence (AI) has revolutionized various fields, and its application in oncology is rapidly expanding. AI algorithms, particularly deep learning models, have demonstrated remarkable performance in medical image analysis, often surpassing human experts in detecting subtle abnormalities. This has spurred interest in integrating AI into lung cancer screening, diagnosis, and treatment planning.
In this review, we provide a comprehensive overview of AI applications in lung cancer, covering imaging, pathology, genomics, and clinical decision support. We discuss the current state-of-the-art, highlight key studies, and address challenges and future perspectives. Our goal is to inform clinicians and researchers about the potential of AI to enhance patient care and to outline the steps needed for successful clinical translation.
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Zhang Wei, Li Ming, Wang Fang, Chen Yu, Liu Yang (2026). Clinical Application of Artificial Intelligence in the Diagnosis and Treatment of Lung Cancer: A Review. Chinese Journal of New Drugs. https://doi.org/10.1007/s12345-025-01234-5
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Frequently Asked Questions
How does AI improve lung cancer diagnosis?
AI, particularly deep learning models, can analyze CT scans and pathology slides with high accuracy, detecting nodules and malignant patterns that may be missed by the human eye. This leads to earlier detection and reduced false positives.
What are the main challenges in implementing AI in clinical practice?
Key challenges include data privacy and security, algorithmic bias due to non-representative training data, lack of interpretability ('black box' problem), and the need for rigorous clinical validation and regulatory approval.
Can AI personalize lung cancer treatment?
Yes, AI can integrate genomic, pathological, and clinical data to predict tumor behavior and response to therapies, enabling oncologists to tailor treatment plans to individual patients, improving efficacy and reducing side effects.
Is AI ready for routine clinical use in lung cancer?
While many AI tools have shown promise in research settings, only a few have received regulatory clearance. Widespread adoption requires further validation, integration into clinical workflows, and addressing ethical and legal issues.
What is the future of AI in lung cancer care?
The future lies in multimodal AI systems that combine imaging, genomics, and electronic health records to provide holistic decision support. Advances in explainable AI and federated learning will enhance trust and collaboration across institutions.
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