ReviewCureus2026
Digital Tools for the Recognition and Triage of Abnormal Uterine Bleeding: A Narrative Review.
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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal uterine bleeding is a common gynaecological presentation, but patients frequently face difficulty distinguishing normal variation from bleeding that requires medical assessment. Digital health tools, including menstrual tracking applications, electronic bleeding diaries, telemedicine, eConsult systems, digital education, decision aids, and artificial intelligence-based risk prediction models, may improve recognition, documentation, triage, referral, and follow-up. This narrative review synthesizes the available literature on digital tools relevant to abnormal uterine bleeding and heavy menstrual bleeding. A structured search of PubMed, Scopus, and Web of Science was conducted in May 2026 using terms for abnormal uterine bleeding, heavy menstrual bleeding, menstrual disorders, digital health, mobile applications, menstrual tracking, symptom checkers, telehealth, decision aids, patient portals, remote monitoring, artificial intelligence, and chatbots. After deduplication, 122 unique records were screened, and 27 articles were included. The evidence shows that digital menstrual tracking can identify abnormal bleeding patterns at scale and may improve symptom histories, while mobile pictorial blood assessment charts and smartphone bleeding diaries offer more structured quantification than recall alone. Telemedicine and eConsult pathways appear feasible for selected abnormal uterine bleeding presentations when supported by safety-netting and clear thresholds for in-person assessment. Digital education and decision aids can improve knowledge and shared decision-making, although evidence for clinical outcomes is limited. Artificial intelligence models show promise for risk stratification, particularly for endometrial pathology, but require external validation, transparency, and clinical governance before routine deployment. Overall, digital tools may support earlier recognition and more efficient triage of abnormal uterine bleeding, but current evidence remains fragmented. Future research should prioritize validated triage algorithms, patient-centred outcomes, equity, privacy, and integration with routine gynaecological workflows.
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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.