Digital Pathology and AI: Advancing Computer-Aided Diagnosis Beyond the Microscope
This Lancet Oncology review examines how the convergence of whole-slide imaging and artificial intelligence is transforming pathology from a microscope-bound discipline into a data-driven diagnostic science. The authors discuss deep learning and hand-crafted feature extraction approaches, their application to cancer diagnosis, and the regulatory and reimbursement challenges facing clinical adoption. The piece frames digital pathology as a platform for unlocking morphometric biomarkers invisible to the human eye.
The original study
Digital pathology and artificial intelligence.
- Authors
- Niazi MKK, Parwani AV, Gurcan MN
- Journal
- The Lancet. Oncology
- Type
- Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
- PMID
- 31044723
Original abstract
In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.