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Artificial intelligence is being studied in colposcopy to improve recognition of cervical intraepithelial lesions using digital images. Early prospective studies suggest that deep-learning systems can achieve clinically useful sensitivity and specificity, particularly when used as decision support alongside expert examination. The major challenge is generalizability: performance can vary with camera systems, image quality, prevalence of disease and operator technique. For gynecologists, AI should therefore be considered an adjunct that can standardize quality assessment or highlight suspicious areas rather than a replacement for colposcopy and histopathology. Prospective validation in diverse populations is essential before autonomous deployment. This technology aligns with AICOG's interest in AI-enabled women's health while keeping clinical accountability with the physician.
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Artificial intelligence is being studied in colposcopy to improve recognition of cervical intraepithelial lesions using digital images. Early prospective studies suggest that deep-learning systems can achieve clinically useful sensitivity and specificity, particularly when used as decision support alongside expert examination. The major challenge is generalizability: performance can vary with camera systems, image quality, prevalence of disease and operator technique. For gynecologists, AI should therefore be considered an adjunct that can standardize quality assessment or highlight suspicious areas rather than a replacement for colposcopy and histopathology. Prospective validation in diverse populations is essential before autonomous deployment. This technology aligns with AICOG's interest in AI-enabled women's health while keeping clinical accountability with the physician.
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