Artificial Intelligence in Pathology.

Abstract

BACKGROUND\nIncreasing digitalization enables the use of artificial intelligence (AI) and machine learning in pathology. However, these technologies have only just begun to be implemented, and no randomized prospective trials have yet shown a benefit of AI-based diagnosis. In this review, we present current concepts, illustrate them with examples from representative publications, and discuss the possibilities and limitations of their use.\n\n\nMETHODS\nThis article is based on the results of a search in PubMed for articles published between January 1950 and January 2020 containing the searching terms artificial intelligence, deep learning, and digital pathology, as well as the authors own research findings.\n\n\nRESULTS\nCurrent research on AI in pathology focuses on supporting routine diagnosis and on prognostication, particularly for patients with cancer. Initial data indicate that pathologists can arrive at a diagnosis faster and more accurately with the aid of a computer. In a pilot study on the diagnosis of breast cancer, involving 70 patients, sensitivity for the detection of micrometastases rose from 83.3% (by a pathologist alone) to 91.2% (by a pathologist combined with a computer algorithm). The evidence likewise suggests that AI applied to histomorphological properties of cells during microscopy may enable the inference of certain genetic properties, such as mutations in key genes and deoxyribonucleic acid (DNA) methylation profiles.\n\n\nCONCLUSION\nInitial proof-of-concept studies for AI in pathology are now available. Randomized, prospective studies are now needed so that these early findings can be confirmed or falsified.