Application of Artificial Intelligence-Assisted Teaching Systems in Gynaecological Cytology Residency Training

Gynaecological cytology diagnosis is a critical component of cervical cancer screening, yet structural challenges, including lengthy training cycles, resource scarcity, and high subjectivity, have long plagued its talent development. This study explores the mechanisms and value of AI-assisted teaching systems in gynaecological cytology training. Grounded in cognitive load theory, it utilizes AI visual enhancement and real-time feedback mechanisms to optimize knowledge internalization pathways. This creates a blended learning ecosystem featuring virtual-physical integration, comprehensive data evaluation, and human-machine collaborative instruction. This approach significantly shortens training cycles, enhances regional diagnostic consistency through standardized training systems, maximizes educational efficiency, and supplies primary healthcare with digitally minded, high-calibre pathology professionals—thereby bolstering cervical cancer prevention and control systems.