AI in Diagnostic Pathology: From Whole-Slide Imaging to FDA-Approved Clinical Tools
This review charts the evolution of digital pathology from whole-slide imaging adoption to FDA-approved AI algorithms, including the landmark clearance of a prostate cancer AI tool. The authors describe how automated WSI scanners now produce diagnostic-quality images that, combined with machine learning, enable integration of anatomical, clinical and molecular pathology data. The piece provides a practical roadmap for incorporating AI into pathology reporting workflows.
The original study
Artificial intelligence in diagnostic pathology.
- Authors
- Shafi S, Parwani AV
- Journal
- Diagnostic pathology
- Type
- Journal Article, Review
- PMID
- 37784122
Original abstract
Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.