ReviewJournal of clinical medicine2024
AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed.
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Role of Scientific Societies in Continuing Education in Cytopathology: Present and Future Perspectives.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Review
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection.bioRxiv : the preprint server for biology · 2026Article
- The Augmented Cytopathologist: A Conceptual Exploratory Narrative Review on Immersive and Vision-Language Models Tools in Digital Pathology.Journal of imaging · 2026Review
- Comparison of human-AI agreement in ASA scoring by gender and duration of clinical experience: a real-world study.BMC medical informatics and decision making · 2026Article
- Cytopathological Findings of Axillary Lymph Nodes in Patients with Breast Cancer in Correlation with Ultrasound Findings.Iranian journal of pathology · 2026Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Public Health: Bridging Today's Trends with Tomorrow's Possibilities.Bioengineering (Basel, Switzerland) · 2025Article
- AI and Interventional Radiology: A Narrative Review of Reviews on Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Liquid-based cytology in the era of multi-omics and artificial intelligence integration.CytoJournal · 2025Article
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.
Indexed as
Identifiers
What OpenQuestion holds
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.