Cytology 2.0: Digital cytopathology, telepathology, and AI implementation

Digital cytology, telecytology, and artificial intelligence (AI) are reshaping cytopathology by expanding access to primary diagnosis, remote rapid on-site evaluation, consultation, quality assurance, education, archiving, and research. This review summarizes the technological basis of digital cytology, its current clinical and educational applications, implementation strategies, telecytology platforms, and emerging AI tools in gynecologic and non-gynecologic cytology. Compared with surgical pathology, digital cytology faces greater technical challenges because cytologic specimens often contain three-dimensional cell clusters, variable thickness, and overlapping cells, which increase demands on focusing, scanning time, image storage, and infrastructure. Recent advances in z-stacking, extended depth of focus, and whole-slide imaging have improved digital visualization, while updated professional guidance has supported broader validation of digital cytology for primary diagnosis and rapid on-site evaluation. Telecytology has shown high diagnostic concordance and has improved access to expert assessment, particularly for rapid on-site evaluation in resource-limited or remote settings. AI applications have shown promising performance in screening, classification, and quality control, especially in cervical cytology, with expanding use in non-gynecologic specimens. However, implementation remains constrained by technical complexity, variable image quality, regulatory and medicolegal concerns, infrastructure requirements, cost, and the need for robust validation and training. Overall, digital cytology and AI are guiding the transition into a Cytopathology 2.0 era characterized by greater connectivity, scalability, and data‑driven practice, although widespread adoption will depend on further standardization, robust validation, and smooth integration into everyday workflows.