AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications

• URL: http://arxiv.org/abs/2501.15489v1
• Date: Sun, 26 Jan 2025 11:32:43 GMT
• Title: AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications
• Authors: Muhammad Aftab, Faisal Mehmood, Chengjuan Zhang, Alishba Nadeem, Zigang Dong, Yanan Jiang, Kangdongs Liu,
• Abstract summary: This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures.
• Score: 0.3937575566991286
• License:
• Abstract: Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

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