Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology

Artificial intelligence (AI) has driven major disruption across multiple domains of clinical medicine and patient care and is fundamentally redrawing the landscape of modern medicine. In this context, cytopathology stands at a critical crossroads, where traditional microscopic evaluation meets the frontier of computational medicine. Conventionally, a successful cytopathology workflow entails intensive manual effort performed under the close supervision of an expert cytopathologist and an experienced, highly competent team of cytotechnologists. With the advancements in medical science driven by the demand for precision and personalized medicine, workload of the cytopathology laboratory is ever increasing by many folds, while there is an alarming decreasing trend in the availability of skilled human resource. A new era of diagnostic precision is emerging, as machine learning and deep learning algorithms take center stage in the laboratory. These systems tend to streamline the workflow by reviewing high-volume slide sets, prioritizing high-risk cases, and offering prognostic insights. These processes are highly dependent on meticulous digitization of cytology smears using whole slide imaging pathology scanners, which in turn enables telecytology, large-scale data sharing, and the development of robust training datasets, all of which accelerate AI innovation. This review provides an overview of the technical processes and applications of AI-based cytopathology algorithms across different organ systems, workflow transformation (from preanalytical to quality control, telecytopathology, and integration with molecular diagnostics), and the various challenges and limitations in their adoption in the routine diagnostic workflow for patient care.