Commercially Available Artificial Intelligence Solutions for Gynaecologic Cytology Screening and Their Integration Into Clinical Workflow

Abstract

Historically, gynaecologic cytology, particularly cervical screening through Pap smear tests, has been instrumental in early cancer detection, but not without its challenges. These include variability in interpretation and the labour-intensive nature of manual screening processes. The advent of artificial intelligence (AI) technologies, especially machine learning and deep learning, heralds a new era in cytology, offering enhanced accuracy, consistency, and efficiency. These advancements promise to mitigate traditional limitations by automating routine analyses, aiding early cancer detection, and reducing the workload of laboratory personnel. This review thoroughly examines the current status of commercial AI software in gynaecologic cytology screening. We critically assess the capabilities, performance, and impact of these AI tools in a clinical context. Additionally, the review addresses the integration challenges and potential of AI in clinical practice, including workflow integration, regulatory compliance, and ethical considerations. Through this comprehensive analysis, we aim to provide insights into how AI is reshaping gynaecologic cytology, paving the way for more effective disease management and enhanced patient care in women's health.

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