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Abstract
Digital pathology, powered by whole-slide imaging technology, has the potential to transform the landscape of cancer research and diagnosis. By converting traditional histopathological specimens into high-resolution digital images, it paves the way for computer-aided analysis, uncovering a new horizon for the integration of artificial intelligence (AI) and machine learning (ML). The accuracy of AI- and ML-driven tools in distinguishing benign from malignant tumors and predicting patient outcomes has ushered in an era of unprecedented opportunities in cancer care. However, this promising field also presents substantial challenges, such as data security, ethical considerations, and the need for standardization. In this review, we delve into the needs that digital pathology addresses in cancer research, the opportunities it presents, its inherent potential, and the challenges it faces. The goal of this review is to stimulate a comprehensive discourse on harnessing digital pathology and AI in health care, with an emphasis on cancer diagnosis and research.
Expected final online publication date for the Annual Review of Cancer Biology, Volume 8 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.