SynthesisLa Radiologia medica2025
Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.
Synthesis in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Modern Polycystic Ovary Syndrome (PCOS) Management: Intelligent Drug Delivery and Metabolic Reprogramming for Ovarian Restoration and Fertility Optimization.Biomolecules · 2026Review
- Machine learning-based prediction of IVF/ICSI outcomes in male factor infertility highlighting couple-level BMI.Frontiers in endocrinology · 2026Article
- AutoPCOS: a stepwise multimodal intelligent framework for polycystic ovary syndrome risk stratification and diagnostic support.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundIntegrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability. PURPOSE: To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts. EVIDENCE REVIEWS: AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.
resultsOur analysis traced the principal applications of AI in PCOS management, focusing on prediction, diagnosis, classification, and screening. Furthermore, this study ventures into the potential of amalgamating and augmenting existing digital health technologies to forge an AI-augmented digital healthcare ecosystem encompassing the prevention and holistic management of PCOS. We also discuss strategic avenues that may facilitate the clinical translation of these innovative systems.
conclusionThis systematic review consolidated the latest advancements in AI-driven PCOS management encompassing prediction, diagnosis, classification, and screening of potential complications, developing a digital healthcare framework tailored to the practical clinical management of PCOS.
Indexed as
Identifiers
What Socratic 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 Socratic graph.