ReviewJournal of cytology
Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.
Review in Journal of cytology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.