Evidence map›Paper›PMID 40986160›Full record

ReviewJournal of assisted reproduction and genetics2025

Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.

RunTang Zhou, YanHong Wei, Yingguan Xiong, BingBing Su, JunHao Xie, Linlin Hu, XiaoCan Lei

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of assisted reproduction and genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

RunTang Zhou *Department of Histology and Embryology, Hengyang Medical School, Clinical Anatomy and Reproductive Medicine Application Institute, University of South China, Hengyang, 421001, Hunan, China.ORCID http://orcid.org/0009-0003-8136-4155
YanHong Wei *Department of Obstetrics and Gynecology, Key Laboratory of Research On Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
Yingguan XiongDepartment of Histology and Embryology, Hengyang Medical School, Clinical Anatomy and Reproductive Medicine Application Institute, University of South China, Hengyang, 421001, Hunan, China.
BingBing SuDepartment of Histology and Embryology, Hengyang Medical School, Clinical Anatomy and Reproductive Medicine Application Institute, University of South China, Hengyang, 421001, Hunan, China.
JunHao XieDepartment of Histology and Embryology, Hengyang Medical School, Clinical Anatomy and Reproductive Medicine Application Institute, University of South China, Hengyang, 421001, Hunan, China.
Linlin HuDepartment of Obstetrics and Gynecology, Key Laboratory of Research On Clinical Molecular Diagnosis for High Incidence Diseases in Western Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China. hutwolin@126.com.
XiaoCan LeiDepartment of Histology and Embryology, Hengyang Medical School, Clinical Anatomy and Reproductive Medicine Application Institute, University of South China, Hengyang, 421001, Hunan, China. liulincun@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Primary ovarian insufficiency (POI) is also known as premature ovarian failure (POF), defined as loss of normal, predictable ovarian activity before the age of 40 years. In addition, POI patients suffer from long-term complications such as osteoporosis, cardiovascular disease, and depression. The impact on women caused by POI and its complications make it become a major health challenge worldwide. The recent advances in digital health technology (DHT), especially artificial intelligence (AI), provide an important opportunity to improve the efficiency of the management of POI. Clinical work has improved the efficiency of healthcare with the assistance of AI, enabling clinicians to improve clinical treatment efficiency, and mitigate the differences in healthcare level caused by suboptimal resource allocation. This article reviews the application progress of AI in the treatment of POI in recent years, and discusses the opportunities and challenges of AI in clinical application. In addition, we explored the integration of existing digital health technology resources to discuss an AI-assisted eco-smart healthcare system for the treatment of POI.

Indexed as

Artificial IntelligencePrimary Ovarian InsufficiencyAge of OnsetFemaleHumansOvarian ReserveRisk FactorsTreatment OutcomeArtificial intelligenceDigital health technologyPrimary ovarian insufficiencyWomen’s health

Identifiers

PMID40986160
PMCPMC12640423

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.