Evidence map›Paper›PMID 34905872›Full record

ArticleObstetrics & gynecology science2022

Artificial intelligence in obstetrics.

Ki Hoon Ahn, Kwang-Sig Lee

Abstract read
In one paragraph

Article in Obstetrics & gynecology science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Prediction for Perioperative Stroke Using Intraoperative Parameters.Journal of the American Heart Association · 2024
    Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Machine learning and disease prediction in obstetrics.Current research in physiology · 2023
    Review
  15. A deep learning mixed-data type approach for the classification of FHR signals.Frontiers in bioengineering and biotechnology · 2022
    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

2 authors.

Ki Hoon AhnDepartment of Obstetrics and Gynecology, Korea University Anam Hospital, Seoul, Korea.
Kwang-Sig LeeAI Center, Korea University Anam Hospital, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

Indexed as

Artificial intelligenceDiagnosisDiseaseFetusMother

Identifiers

PMID34905872
PMCPMC8942755

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.