Mitotic counting is a crucial component of breast cancer diagnosis, but traditional methods are labor-intensive and error-prone. The introduction of AI in digital pathology has transformed the process by providing greater accuracy and efficiency. AI algorithms can standardize the identification of hotspots and automatically compute mitotic counts, reducing the workload of pathologists. However, for AI to be effective in clinical practice, it requires high-quality training data and ongoing validation by practicing pathologists. Additionally, addressing non-standardized data collection, particularly in racial and ethnic disparities, is a key hurdle for the FDA approval of AI algorithms in pathology. The integration of AI in breast cancer diagnostics presents new opportunities for more precise and efficient diagnoses.
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Mitotic Counting Reimagined: When AI Meets the Microscope
