Role of AI in Cancer Screening and Its Detection
Muskan, S. Sharma, Parul Sharma, Manoj Malik, Jaspreet Kaur · 2025
This chapter explores the transformative role of Artificial Intelligence (AI) in cancer screening and early detection, addressing the limitations of conventional diagnostic methods and showcasing advancements brought by AI-driven technologies. Traditional screening techniques such as mammography, ultrasound, MRI, and liquid biopsy have significantly improved early cancer detection; however, they often suffer from drawbacks like false positives, interpretation variability, and limited sensitivity for certain cancer types. The integration of AI, particularly through machine learning (ML) and deep learning (DL) models, has revolutionized cancer diagnostics. AI systems have demonstrated the ability to analyze large, complex datasets—including imaging and genetic information—with greater precision and consistency compared to traditional approaches. The chapter examines various AI applications across multiple cancer types, highlighting AI models developed for breast cancer, lung cancer, skin cancer, gastric cancer, and prostate cancer screening. Special attention is given to the role of Convolutional Neural Networks (CNNs) in processing medical images and identifying malignancies with high accuracy. AI-assisted diagnostics not only improve the sensitivity and specificity of detection but also offer faster analysis times and reduce the workload on clinicians. Furthermore, AI enables personalized screening strategies by leveraging patient-specific risk profiles, thus paving the way for precision medicine. The chapter also discusses the challenges hindering the widespread adoption of AI in clinical practice, including data quality issues, lack of standardized protocols, algorithmic bias, and ethical considerations. Proposed strategies to address these barriers include developing explainable AI models, ensuring diversity in training datasets, and promoting regulatory frameworks that support AI integration without compromising patient safety.