Digital Pathology and Artificial Intelligence

By Muhammad Farooq Khan Niazi, Anil V. Parwani, Metin Nafi Gurcan. The Lancet Oncology, 2019

1,006 citations recorded by the source corpus.

In modern clinical practice, digital pathology plays a crucial role and is increasingly a technological requirement in the laboratory environment. The advent of whole slide imaging, the availability of faster networks, and cheaper storage solutions make it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were only limited to radiology and cardiology in the past. With integration of digital slides into the pathology workflow, advanced algorithms and computer-aided diagnostic techniques extend the frontiers of the pathologist’s view beyond a microscopic slide and enable true utilization and integration of knowledge beyond human limits and boundaries. There is clear potential for artificial intelligence breakthroughs in digital pathology. In this review article, we discuss recent advancements in digital slide based image diagnosis along with some challenges and opportunities for artificial intelligence in digital pathology.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fdf638e7de55475c452e6db954a565f5ead881db

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Citations in the corpus

Early detection of cancer (2022, positive): “Digital pathology and could help improve test turnaround times and diagnostic accuracy, detecting early signs of cancer and providing data for further research ( 111 ).”
Deep neural network models for computational histopathology: A survey (2019, background): “The success of DL depends on the availability of highquality training sets to achieve the desired predictive performance (Madabhushi and Lee, 2016; Bera et al., 2019; Niazi et al., 2019).”
Artificial intelligence (AI) and big data in cancer and precision oncology (2020, methodology): “Notably, the majority of deep learning algorithms are criticized for being unable to explain the rationale behind their decisions, hence they are labelled as black boxes [49].”