ArticleHuman reproduction (Oxford, England)2026
The problem with the 'truth': rethinking ground truth for artificial intelligence in endometriosis diagnosis.
Article in Human reproduction (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Superficial Peritoneal Endometriosis Beyond Surgical Diagnosis: A Narrative Review of Emerging Functional and Molecular Perspectives.Medicina (Kaunas, Lithuania) · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
Artificial intelligence (AI) is revolutionizing how we practice medicine. In areas where we have traditionally struggled, such as diagnosing endometriosis, AI has significant potential to improve the breadth and accuracy of diagnostic services offering a great benefit to patient care. When developing AI models for diagnosis, the 'ground truth' refers to the reference standard used in the labelling of the data used to train the model. Conventionally, in clinical medicine, we correlate any new diagnostic tool to the established 'gold standard', which in the case of endometriosis is laparoscopic visualization of lesions and histological confirmation. This method however is increasingly recognized as imperfect. Acknowledgement of the limitations of surgery and recent improvements in the diagnostic capability of imaging technologies to detect endometriosis, has created a situation where endometriosis no longer has one clear 'gold standard' for diagnosis. In this commentary, we will explore the impact of this on AI-driven endometriosis diagnostic tools and propose novel ways this could be addressed in the context of creating ground truths for endometriosis diagnosis.
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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 Socratic graph.