Artificial intelligence in cancer pathology: Applications, challenges, and future directions

Abstract

The application of artificial intelligence (AI) in cancer pathology has shown significant potential to enhance diagnostic accuracy, streamline workflows, and support precision oncology. This review examines the current applications of AI across various cancer types, including breast, lung, prostate, and colorectal cancer, where AI aids in tissue classification, mutation detection, and prognostic predictions. The key technologies driving these advancements include machine learning, deep learning, and computer vision, which enable automated analysis of histopathological images and multi-modal data integration. Despite these promising developments, challenges persist, including ensuring data privacy, improving model interpretability, and meeting regulatory standards. Furthermore, this review explores future directions in AI-driven cancer pathology, including real-time diagnostics, explainable AI, and global accessibility, emphasizing the importance of collaboration between AI and pathologists. Addressing these challenges and leveraging AI's full potential could lead to a more efficient, equitable, and personalized approach to cancer care.

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