Challenges in the Development, Deployment & Regulation of Artificial Intelligence (AI) in Anatomical Pathology.

Artificial intelligence (AI), deep learning, and other machine learning approaches have made significant advances in recent years, finding applications in almost every industry, including healthcare. AI has proven to be capable of a spectrum of mundane to complex medically oriented tasks previously only performed by boarded physicians, most recently assisting detection of difficult-to-find cancer on histopathology slides. Although computers will not replace pathologists anytime soon, properly designed AI-based tools hold great potential to increase workflow efficiency and diagnostic accuracy in the practice of pathology. Recent trends, such as data augmentation, crowd-sourcing to generate annotated datasets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role for pathologists in algorithm development. Proper integration of AI-based systems into anatomical 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, 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, should 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 anatomical pathology.