ReviewCureus2026
Integration of Artificial Intelligence in Endometrial Cancer Management.
Review in Cureus, 2026. 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
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
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
Endometrial cancer is one of the most frequent gynecologic malignancies in developed countries, with increasing incidence worldwide. The complexity of molecular and clinicopathologic data has highlighted the need for advanced analytical tools to optimize risk assessment and therapeutic decision-making. Artificial intelligence (AI) has emerged as a disruptive technology in oncology, enabling high-dimensional data integration and predictive modeling. Its application in endometrial cancer holds significant potential to improve diagnosis, prognostic stratification, and individualized treatment strategies. This narrative review synthesizes evidence from original research articles, systematic reviews, meta-analyses, and emerging translational studies focusing on AI applications in endometrial cancer. It evaluates AI-driven approaches in screening and early detection, machine learning-based diagnostic and risk stratification models, radiomics and imaging analytics, deep learning applications in histopathology and molecular classification, and predictive algorithms for treatment planning and prognostic assessment. A marked increase in scientific publications over the past decades underscores the expanding role of AI in endometrial cancer research. AI-based systems leveraging multimodal data including clinical variables, imaging radiomics, digital histopathology, and molecular profiling demonstrate improved performance in diagnostic classification, risk prediction, and treatment optimization. Enhanced accuracy in tumor grading, molecular subtype prediction, and survival modeling compared with conventional statistical approaches was reported. AI represents a real opportunity to enhance the comprehensive management of endometrial cancer. Nevertheless, its clinical adoption depends on rigorous external validation, harmonization of datasets, transparency of algorithms, and integration into multidisciplinary decision-making pathways that preserve clinical judgment and ensure equitable access to innovation.
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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.