Published Paper Details:

The early detection of lung cancer is crucial for improving patient outcomes and reducing mortality rates. In recent years, artificial intelligence (AI) and machine learning (ML) techniques have emerged as powerful tools for enhancing the accuracy and efficiency of lung cancer detection. This abstract provides an overview of the key findings and advancements in AI/ML based approaches for early detection of lung cancer. AI/ML techniques offer the ability to analyze diverse datasets, including medical imaging data, genomic profiles, and clinical variables, to identify individuals at high risk of developing lung cancer. Convolutional neural networks (CNNs) and transfer learning methods have shown promise in analyzing chest CT scans and X-ray images to detect lung nodules and abnormalities with high sensitivity and specificity. Integration of biomarker data, such as genomic mutations and circulating tumor DNA (ctDNA) profiles, further enhances the accuracy of lung cancer detection models. Multimodal fusion techniques, ensemble learning methods, and realtime decision support systems enable comprehensive analysis and interpretation of data, facilitating early detection and intervention. Future research directions include the integration of multiomics data, longitudinal monitoring strategies, and personalized risk assessment models to improve the effectiveness of lung cancer screening programs. Challenges such as data standardization, interpretability of AI models, and ethical considerations must be addressed to ensure the responsible deployment of AI/ML techniques in clinical practice. Collaboration between researchers, clinicians, and industry partners is essential for advancing the field and translating research findings into actionable insights for improving patient care. In conclusion, AI/ML techniques hold immense promise for revolutionizing the early detection of lung cancer. By leveraging emerging technologies and interdisciplinary collaboration, we can enhance screening strategies, enable personalized interventions, and ultimately, save lives through early diagnosis and treatment of lung cancer.