Digital pathology and artificial intelligence

• Published in: The lancet oncology Vol. 20; no. 5; pp. e253 - e261
• Main Authors: Niazi, Muhammad Khalid Khan, Parwani, Anil V, Gurcan, Metin N
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2019; Elsevier Limited
• Subjects: Algorithms; Artificial Intelligence; Automation; Cancer; Diagnosis; Diagnosis, Computer-Assisted; Histopathology; Humans; Image Interpretation, Computer-Assisted; Integration; Learning algorithms; Microscopy; Neoplasms - pathology; Neoplasms - therapy; Oncology; Pathology; Predictive Value of Tests; Prognosis; Quality control; Reproducibility of Results; Workflow
• ISSN: 1470-2045; 1474-5488; 1474-5488
• DOI: 10.1016/S1470-2045(19)30154-8

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. 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 utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.