ReviewFrontiers in oncology2025
Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.
Review in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.Cureus · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Integrating Biomarkers into Cervical Cancer Screening-Advances in Diagnosis and Risk Prediction: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early screening and diagnosis, precise treatment and prognosis prediction are pivotal for enhancing cancer patients' survival rates and for improving their life quality, it is the same case in gynecological malignancies. Among gynecological malignancies, endometrial cancer, cervical cancer and ovarian cancer are regarded as the three major types due to their high incidence rates, clinical severity and unique biological characteristics. Nowadays, the early screening and diagnosis of gynecological malignancies mainly depends on imaging examinations, pathological evaluations, and serum biomarkers assessments, while they possess inherent limitations. The treatment and prognosis prediction of gynecological malignant tumors show significant individualized differences. Although the treatment methods have been continuously improved, there are still shortcomings such as complex drug resistance mechanisms that limit the treatment effect, the impact of treatment toxicity on the quality of life of patients, and the impact of varying doctor experience levels across hospitals on disparities in diagnosis and treatment quality. With the rapid evolution of artificial intelligence (AI), particularly through the integration of deep learning (DL) and machine learning (ML) algorithms, AI technologies have shown their benefits in medicine. AI technologies can efficiently analyze medical images, genomic data, and clinical information, thereby enable more precise diagnoses, facilitate the design of personalized treatment strategies, predict treatment outcomes and recurrence risks. AI has also been utilized in gynecological malignancies and exhibited substantial potential. This review summarizes the latest advancements of AI applications in the early screening, diagnosis, treatment and prognosis prediction of three central gynecological malignancies and dialectically discussed current limitations. It provides valuable insights into the future translational potential of AI in gynecological oncology.
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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 Socratic graph.