Clinical-grade autonomous cytopathology via whole-slide edge tomography

Cytopathology plays a central role in the early detection of cancers such as cervical, lung, and bladder cancer due to its speed, simplicity, and minimally invasive nature. However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation. Although many AI-powered systems have been proposed to improve consistency, none have achieved fully autonomous, clinical-grade performance. Existing approaches serve as assistive tools and still rely on human oversight for interpretation and decision-making. 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. In addition to supporting cell-level classification, the platform enables flow cytometry-like, population-wide morphological profiling for comprehensive interpretation of cellular distributions and patterns. A vision transformer achieved area-under-receiver-operating-characteristic-curve values exceeding 0.99 for detecting LSIL, HSIL, and adenocarcinoma. In a clinical cohort of cervical liquid-based cytology samples from 318 donors, LSIL counts strongly correlated with HPV positivity, while HSIL counts scaled with diagnostic severity. The system enables autonomous triage cytology, offering a foundation for routine, scalable, and objective diagnostics.