AI in Pathology: Current Clinical Applications and the Road to Precision Medicine
This comprehensive review covers the current and prospective applications of artificial intelligence in clinical pathology, examining its role in diagnosis, prognosis, workflow efficiency and pathologist education. The authors analyse why translational progress from research to clinical deployment has been slow and identify regulatory, validation and adoption factors that must be addressed for AI to reshape pathology services.
The original study
Current and future applications of artificial intelligence in pathology: a clinical perspective.
- Authors
- Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, et al.
- Journal
- Journal of clinical pathology
- Type
- Journal Article, Review
- PMID
- 32763920
Original abstract
During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.