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**AI in pathology** uses **computer vision to analyze tissue samples and cellular images** — detecting cancer cells, grading tumors, identifying biomarkers, and quantifying disease features in biopsy slides, augmenting pathologist expertise to improve diagnostic accuracy, consistency, and throughput in anatomic pathology. **What Is AI in Pathology?** - **Definition**: Deep learning applied to digital pathology images. - **Input**: Whole slide images (WSI) of tissue biopsies, cytology samples. - **Tasks**: Cancer detection, tumor grading, biomarker quantification, mutation prediction. - **Goal**: Faster, more accurate, more consistent pathology diagnosis. **Key Applications** **Cancer Detection**: - **Task**: Identify cancer cells in tissue samples. - **Cancers**: Breast, prostate, lung, colon, skin, lymphoma. - **Performance**: Matches or exceeds pathologist accuracy. - **Example**: PathAI detects breast cancer metastases with 99% accuracy. **Tumor Grading**: - **Task**: Assess cancer aggressiveness (Gleason score for prostate, Nottingham for breast). - **Benefit**: Reduce inter-pathologist variability (20-30% disagreement). - **Impact**: More consistent treatment decisions. **Biomarker Quantification**: - **Task**: Measure PD-L1, HER2, Ki-67, other markers for treatment selection. - **Method**: Count positive cells, calculate percentages. - **Benefit**: Objective, reproducible measurements vs. subjective scoring. **Mutation Prediction**: - **Task**: Predict genetic mutations from tissue morphology. - **Example**: Predict MSI status, EGFR mutations without molecular testing. - **Benefit**: Faster, cheaper than genomic sequencing. **Margin Assessment**: - **Task**: Check if tumor completely removed during surgery. - **Speed**: Intraoperative analysis in minutes vs. days. - **Impact**: Reduce need for repeat surgeries. **Digital Pathology Workflow** **Slide Scanning**: - **Process**: Physical slides scanned at 20-40× magnification. - **Output**: Gigapixel whole slide images (WSI). - **Scanners**: Leica, Philips, Hamamatsu, Roche. **AI Analysis**: - **Process**: Deep learning models analyze WSI. - **Architecture**: Convolutional neural networks, vision transformers. - **Challenge**: Gigapixel images require specialized processing. **Pathologist Review**: - **Workflow**: AI highlights regions of interest, suggests diagnosis. - **Pathologist**: Reviews AI findings, makes final diagnosis. - **Interface**: Digital microscopy software with AI overlays. **Benefits**: Improved accuracy, reduced turnaround time, objective quantification, second opinion, extended expertise. **Challenges**: Digitization costs, regulatory approval, pathologist adoption, stain variability, rare disease training data. **Tools & Platforms**: PathAI, Paige.AI, Proscia, Ibex Medical Analytics, Aiforia, Visiopharm.