ReviewFrontiers in medicine2026
Digital health-enabled life-course management of endometriosis: from early recognition and assisted diagnosis to symptom monitoring and self-management.
Review in Frontiers in medicine, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis is a chronic inflammatory disease that often requires longitudinal care across recognition, diagnostic assessment, symptom control, fertility planning, treatment transitions, and recurrence reassessment. The information required for these decisions is frequently fragmented across patient experience, clinical encounters, imaging, treatment history, and health-system records. This narrative Review synthesizes digital-health applications in endometriosis according to clinical function, data source, and translational maturity. Literature was identified through structured searches of major biomedical databases and targeted reference screening, with emphasis on clinically relevant validation, prospective evaluation, and implementation evidence. The most mature applications include structured electronic symptom capture, electronic patient-reported outcomes, selected app- or telehealth-based support, and digital pain interventions. AI/ML approaches for imaging, risk estimation, molecular classification, fertility outcomes, and recurrence prediction are technically active but are commonly limited by internal validation, uncertain calibration and action thresholds, and sparse prospective clinical-utility evidence. We propose a clinician-supervised life-course framework in which patient-expressed data, clinical and imaging information, treatment history, fertility goals, and follow-up data are integrated to support early recognition, assisted diagnosis, monitoring, shared decisions, and reassessment. The target state is clinical coherence rather than maximal data capture, supported by explicit institutional responsibility for review, escalation, feedback, privacy, equity, and human oversight. Progress toward routine use will depend on external validation, interoperable data architecture, low-burden measurement, prospective pathway evaluation, and accountable implementation.
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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.