Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review)
TL;DR
The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems.
Abstract
The integration of artificial intelligence (AI) into digital pathology is perhaps the most revolutionary leap forward in modern diagnostic medicine. The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems. AI systems have achieved pathologist-level performance in controlled settings, including diagnostic accuracy >99% and area under the receiver operating characteristic curve values exceeding 0.97. However, translating research into clinical adoption is riddled with several challenges attributable to computational requirements, data standardization issues, regulatory hurdles and limitations in generalizability. Moreover, Vision Transformers are widely popular as powerful alternatives to conventional convolutional models, delivering high performance in certain domains while also imposing a novel computational burden. Overcoming these challenges is a prerequisite for the successful integration of AI into pathology practice and the realization of its full diagnostic potential.