Artificial intelligence in lung cancer: current applications, future perspectives, and challenges

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomics, radiomics, and pathomics in lung cancer research. The review elucidates AI's transformative role in enhancing early detection, personalizing treatment strategies, and accelerating therapeutic innovations. We explore AI's impact on precision medicine in lung cancer, encompassing early diagnosis, treatment planning, monitoring, and drug discovery. The potential of AI in analyzing complex datasets, including genetic profiles, imaging data, and clinical records, is discussed, highlighting its capacity to provide more accurate diagnoses and tailored treatment plans. Additionally, we examine AI's potential in predicting patient responses to immunotherapy and forecasting survival rates, particularly in non-small cell lung cancer (NSCLC). The review addresses technical challenges facing AI implementation in lung cancer care, including data quality and quantity issues, model interpretability, and ethical considerations, while discussing potential solutions and emphasizing the importance of rigorous validation. By providing a comprehensive analysis for researchers and clinicians, this review underscores AI's indispensable role in combating lung cancer and its potential to usher in a new era of medical breakthroughs, ultimately aiming to improve patient outcomes and quality of life.