ReviewWomen's health (London, England)
Assessing the Utility of artificial intelligence in endometriosis: Promises and pitfalls.
Review in Women's health (London, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Superficial Peritoneal Endometriosis Beyond Surgical Diagnosis: A Narrative Review of Emerging Functional and Molecular Perspectives.Medicina (Kaunas, Lithuania) · 2026Review
- Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.Diagnostics (Basel, Switzerland) · 2026Review
- Interpretable machine learning for endometriosis classification: a rule-based approach.BMC medical informatics and decision making · 2026Article
- Translational Assessment of Omics Approaches in Endometriosis: Bridging Molecular Discovery with Clinical Utility.International journal of molecular sciences · 2026Review
- Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers.Abdominal radiology (New York) · 2026Review
- Transdisciplinary Approach to Endometriosis-Related Chronic Pelvic Pain in Colombia: A Consensus Statement for Patient-Centered CareRevista colombiana de obstetricia y ginecologia · 2026Article
- Non-invasive endometriosis staging prediction using integrated radiomics and spatiotemporal transformer model based on dynamic contrast-enhanced MRI.Scientific reports · 2026Article
- Navigating complexity: review and updates of endometriosis and adenomyosis imaging.The British journal of radiology · 2026Review
- Current Insights into Endometriosis: Hormonal Management, Clinical Outcomes, and Opportunities for Progress.Current pharmaceutical design · 2026Review
- Dysregulation of Key Biological Processes in Endometriosis Pathophysiology.Current molecular medicine · 2026Review
- Development of an AI-based magnetic resonance imaging reading support program (AMP) for deep endometriosis diagnosis.Scientific reports · 2025Article
- Multimodal large language models for women's reproductive mental health.Archives of women's mental health · 2025Review
- Potential biomarkers for early detection of endometriosis: current state of art (what we know so far).Journal of applied genetics · 2025Review
- Growth Arrest-Specific Protein 6 Is Elevated in Endometriosis but Shows Poor Diagnostic Performance.International journal of molecular sciences · 2025Article
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- Endometriosis in Adolescence: A Narrative Review of the Psychological and Clinical Implications.Diagnostics (Basel, Switzerland) · 2025Review
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
Endometriosis, a chronic condition characterized by the growth of endometrial-like tissue outside of the uterus, poses substantial challenges in terms of diagnosis and treatment. Artificial intelligence (AI) has emerged as a promising tool in the field of medicine, offering opportunities to address the complexities of endometriosis. This review explores the current landscape of endometriosis diagnosis and treatment, highlighting the potential of AI to alleviate some of the associated burdens and underscoring common pitfalls and challenges when employing AI algorithms in this context. Women's health research in endometriosis has suffered from underfunding, leading to limitations in diagnosis, classification, and treatment approaches. The heterogeneity of symptoms in patients with endometriosis has further complicated efforts to address this condition. New, powerful methods of analysis have the potential to uncover previously unidentified patterns in data relating to endometriosis. AI, a collection of algorithms replicating human decision-making in data analysis, has been increasingly adopted in medical research, including endometriosis studies. While AI offers the ability to identify novel patterns in data and analyze large datasets, its effectiveness hinges on data quality and quantity and the expertise of those implementing the algorithms. Current applications of AI in endometriosis range from diagnostic tools for ultrasound imaging to predicting treatment success. These applications show promise in reducing diagnostic delays, healthcare costs, and providing patients with more treatment options, improving their quality of life. AI holds significant potential in advancing the diagnosis and treatment of endometriosis, but it must be applied carefully and transparently to avoid pitfalls and ensure reproducibility. This review calls for increased scrutiny and accountability in AI research. Addressing these challenges can lead to more effective AI-driven solutions for endometriosis and other complex medical conditions.
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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 OpenQuestion graph.