Deep learning in oncology: Transforming cancer diagnosis, prognosis, and treatment

Abstract

Deep learning (DL) has emerged as a transformative force in oncology, offering unprecedented capabilities in cancer diagnosis, treatment planning, and prognosis. The integration of DL models with vast and complex datasets, including genomic, transcriptomic, and imaging data, has paved the way for more precise and personalized cancer care. In particular, DL's application in drug efficacy and toxicity prediction is gaining traction, addressing the critical challenge of high drug failure rates in clinical development. By leveraging large datasets and sophisticated algorithms, DL models can predict drug responses and optimize treatment strategies, ultimately improving patient outcomes. Additionally, DL-driven automation in medical imaging processing and report generation is revolutionizing radiology, enhancing diagnostic accuracy and consistency. This review explores the current advancements in DL applications across various aspects of oncology, emphasizing the potential of AI-driven tools to enhance the accuracy, efficiency, and personalization of cancer care. Despite the significant progress, challenges such as model validation, ethical considerations, and the need for transparent AI systems remain. Addressing these challenges will be crucial in realizing the full potential of DL in transforming oncology practices.