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This study evaluates an AI model designed to reduce reliance on immunohistochemistry (IHC) for prostate cancer diagnosis by accurately classifying atypical glands in H&E stained tissue sections. The AI model demonstrated high diagnostic accuracy, achieving area under the curve values between 0.951 and 0.993, and successfully reduced IHC usage by 44.4%, 42.0%, and 20.7% across three validation cohorts without any false negative predictions. The findings suggest that AI can optimize IHC use in prostate pathol

Author

Gek Nissa

Language

EN