Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will likely not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/ pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.
Digital displays (monitors) are an indispensable component of a pathologists’ daily workflow, from writing reports, viewing whole-slide images, or browsing the Internet. Due to a paucity of literature and experience surrounding display use and standardization in pathology, the Food and Drug Administration's (FDA) has currently restricted FDA-cleared whole-slide imaging systems to a specific model of display for each system, which at this time consists of only medical-grade (MG) displays. Further, given that a pathologists’ display will essentially become their new surrogate “microscope,” it becomes exceedingly important that all pathologists have a basic understanding of fundamental display properties and their functional consequences. This review seeks to: (a) define and summarize the current and emerging display technology, terminology, features, and regulation as they pertain to pathologists and review the current literature on the impact of different display types (e.g. MG vs. consumer off the shelf vs. professional grade) on pathologists’ diagnostic performance and (b) discuss the impact of the recent digital pathology device componentization and the coronavirus disease 2019 public emergency on the pixel pathway and display use for remote digital pathology. Display technology has changed dramatically over the past 20 years and continues to change at a rapid rate. There is a paucity of published studies to date that investigate how display type affects pathologist performance, with more research necessary in order to develop standards and minimum specifications for displays in digital pathology. Given the complexity of modern displays, pathologists must become better informed regarding display technology if they wish to have more choice over their future “microscopes.”
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.