ReviewComputational and structural biotechnology journal2024
Role of artificial intelligence in digital pathology for gynecological cancers.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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Who cites it
21 citing papers in PubMed, 28 citations in OpenAlex.
- Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.Scientific reports · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Association between Urinary-Extracellular-Vesicle-Enriched Proteome Dynamics and Oncological Outcomes Following Concurrent Chemoradiation in Locally Advanced Cervical Cancer.Computational and structural biotechnology journal · 2026Article
- Ovarian tissue quality assessment and fertility preservation strategies enabled by multi-omics and artificial intelligence: current applications and clinical perspectives.Frontiers in physiology · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Biological Data Resources and Machine Learning Frameworks for Hematology Research.Genomics, proteomics & bioinformatics · 2025Review
- Review
- Challenges and Opportunities in Cytopathology Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Review
- Histolytics: A panoptic spatial analysis framework for interpretable histopathology.Computational and structural biotechnology journal · 2025Article
- Deep learning models for cervical cancer subtyping using whole slide images.Frontiers in oncology · 2025Article
- The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges.Open medicine (Warsaw, Poland) · 2025Article
- A bibliometric analysis of the advance of artificial intelligence in medicine.Frontiers in medicine · 2025Article
- AI-powered advances in type II endometrial cancer: global trends and African contexts.Frontiers in oncology · 2025Review
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Exploratory Analysis of Radiomics and Pathomics in Uterine Corpus Endometrial Carcinoma.Scientific reports · 2024Article
- Global burden of gynaecological cancers in 2022 and projections to 2050.Journal of global health · 2024Article
- Synthetic Genitourinary Image Synthesis via Generative Adversarial Networks: Enhancing Artificial Intelligence Diagnostic Precision.Journal of personalized medicine · 2024Article
- Organoids research progress in gynecological cancers: a bibliometric analysis.Frontiers in oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
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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.