Deep learning in histopathology: the path to the clinic

• Published in: Nature medicine Vol. 27; no. 5; pp. 775 - 784
• Main Authors: van der Laak, Jeroen, Litjens, Geert, Ciompi, Francesco
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.05.2021; Nature Publishing Group
• Subjects: 692/308/575; 692/700/139/422; Algorithms; Artificial intelligence; Biomedical and Life Sciences; Biomedicine; Cancer Research; Computer-aided medical diagnosis; Deep Learning; Histology, Pathological; Histopathology; Humans; Infectious Diseases; Learning algorithms; Machine learning; Metabolic Diseases; Methods; Molecular Medicine; Neurosciences; Pathology, Molecular - methods; Precision Medicine - methods; Review Article; Technology application; Netherlands
• ISSN: 1078-8956; 1546-170X; 1546-170X
• DOI: 10.1038/s41591-021-01343-4

Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value. Recent advances in machine learning techniques have created opportunities to improve medical diagnostics, but implementing these advances in the clinic will not be without challenge.