Evidence map›Paper›PMID 38907128›Full record

ReviewReproductive sciences (Thousand Oaks, Calif.)2024

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Pulkit Verma, Pratibha Maan, Rohit Gautam, Taruna Arora

Abstract readReview
PubMed Publisher
In one paragraph

Review in Reproductive sciences (Thousand Oaks, Calif.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 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

16 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Pooled it
  5. Living with PCOS: A Narrative of its Biology, Diagnosis, and Evolving Treatment.Endocrine, metabolic & immune disorders drug targets · 2026
    Review
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  7. Article
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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

4 authors.

Pulkit Verma *ICMR Headquarters, New Delhi, 110029, India.ORCID 0000-0002-5162-4863
Pratibha Maan *ICMR Headquarters, New Delhi, 110029, India.ORCID 0000-0002-2410-5312
Rohit GautamICMR Headquarters, New Delhi, 110029, India.ORCID 0000-0002-4272-9057
Taruna AroraICMR Headquarters, New Delhi, 110029, India. tarunakatyal@gmail.com.ORCID 0000-0001-7732-9451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

Indexed as

Artificial IntelligencePolycystic Ovary SyndromeFemaleHumansMachine LearningArtificial IntelligenceDiagnosisEndocrine and Metabolic DisordersFemale Reproductive HealthMachine Learning ToolsPolycystic Ovary Syndrome

Identifiers

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.