• Abstract Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing to its speed, simplicity and minimally invasive nature1,2,3,4,5,6,7,8,9. • However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation10,11,12,13,14,15,16,17,18,19,20,21. • Although many artificial intelligence (AI)-powered systems have been proposed to improve consistency22,23,24,25,26, none have achieved fully autonomous, clinical-grade performance. • Existing approaches serve as assistive tools and still rely on human oversight for interpretation and decision-making22,23,24,25,26. • Here we present a clinical-grade autonomous cytopathology pipeline that combines high-resolution, real-time optical whole-slide tomography with edge computing to deliver end-to-end automation. • The system achieves practical performance in imaging speed, quality and data volume, with localized data compression enabling streamlined storage and accelerated AI-driven analysis.
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