| Abstract: |
Machine learning and artificial intelligence are poised to transform pathology. Technologic advances have continued to develop various pathology subdomains such as surgical pathology, cytology, molecular, and laboratory medicine. Pathology includes substantial imaging and non-imaging data that can be used to develop machine learning tools or provide input data for pre-trained clinical tools and have outputs infer clinical decision making, research, or education. While there are proven technologies, there remain clear and present hindrances to understand and overcome. This review will cover many applications of machine learning in pathology in various subdomains, use cases that are available to support clinical decision making, and discuss challenges related to the feasibility and implementation of such systems. © 2022 The Author(s) |
