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Nam, J.G., Park, S., Hwang, E.J., Lee, J.H., Jin, K.N., Lim, K.Y., Vu, T.H., Sohn, J.H., Hwang, S., Goo, J.M., et al. (2019) Development and Validation of a Deep Learning Model for Screening Pneumonia on Chest Radiograph. Radiology, 292, 211-220.
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TITLE:
AI: The Effectiveness of Early Cancer Detection Programs Using AI Algorithms
AUTHORS:
Sara Pawlusek
KEYWORDS:
AI Algorithms, Early Cancer Detection
JOURNAL NAME:
Advances in Breast Cancer Research,
Vol.14 No.2,
April
16,
2025
ABSTRACT: Early detection is important for cancer control, improving patient survival and reducing unnecessary treatment. Methods for early detection of cancer include screening programs, risk stratification, and rapid diagnostic pathways. Nevertheless, limited diagnostic workforces, variability in the added value of screening efficiency, and delays in referral remain impediments to appropriate early detection, especially in post-pandemic healthcare environments. Machine learning (ML) and artificial intelligence (AI) have recently become transformative approaches that can help overcome these limitations for improved diagnostic accuracy, workflow efficiency, and patient selection for screening programs when applied to complex health data. Deep learning algorithms and convolutional neural networks are among the data-driven models that have yielded promising results in manipulating radiological images, pathology slides, and electronic health records for better risk stratification and early diagnosis. AI applications also help to identify cancer recurrence and automate clinical workflows where there are capacity limitations. Even considering these developments, sustainable research into ethical dilemmas, patient data security, biases in AI training datasets, and regulatory compliance represent key areas for ongoing investigations. This review discusses AI in the context of early cancer detection, its use in screening and diagnosis, and the barriers to broader clinical implementation.