Computer-assisted screening platforms using whole slide imaging and artificial intelligence are demonstrating viable performance characteristics for Pap test analysis, potentially addressing critical workforce shortages in cervical cancer detection. Several AI systems have moved beyond experimental phases to show practical utility in clinical validation studies, with some achieving sensitivity and specificity metrics comparable to traditional manual screening methods. This technological advancement addresses a pressing global health challenge, particularly relevant given that cervical cancer represents the fourth leading cancer diagnosis among women worldwide, with disproportionate impact in medically underserved regions. The emergence of validated AI screening tools could democratize access to quality cytological analysis in areas lacking specialized pathology expertise. However, the clinical deployment of these systems faces significant implementation hurdles including regulatory approval pathways, integration with existing laboratory workflows, and establishing appropriate quality control protocols. While early performance data appears encouraging, the technology remains in relative infancy with limited long-term clinical outcome studies. The most promising applications may initially focus on pre-screening workflows to prioritize cases requiring expert human review, rather than full autonomous diagnosis.