Evidence map›Paper›PMID 38247132›Full record

ArticleObstetrics & gynecology science2024

Potential applications of ChatGPT in obstetrics and gynecology in Korea: a review article.

YooKyung Lee, So Yun Kim

Abstract read
In one paragraph

Article in Obstetrics & gynecology science, 2024. 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. Review
  3. Article
  4. Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026
    Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
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  11. Review
  12. Prediction for Perioperative Stroke Using Intraoperative Parameters.Journal of the American Heart Association · 2024
    Article
  13. Article
  14. 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

2 authors.

YooKyung LeeDivision of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, MizMedi Hospital, Seoul, Korea.
So Yun KimDivision of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, MizMedi Hospital, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of chatbot technology, particularly chat generative pre-trained transformer (ChatGPT) with an impressive 175 billion parameters, has garnered significant attention across various domains, including Obstetrics and Gynecology (OBGYN). This comprehensive review delves into the transformative potential of chatbots with a special focus on ChatGPT as a leading artificial intelligence (AI) technology. Moreover, ChatGPT harnesses the power of deep learning algorithms to generate responses that closely mimic human language, opening up myriad applications in medicine, research, and education. In the field of medicine, ChatGPT plays a pivotal role in diagnosis, treatment, and personalized patient education. Notably, the technology has demonstrated remarkable capabilities, surpassing human performance in OBGYN examinations, and delivering highly accurate diagnoses. However, challenges remain, including the need to verify the accuracy of the information and address the ethical considerations and limitations. In the wide scope of chatbot technology, AI systems play a vital role in healthcare processes, including documentation, diagnosis, research, and education. Although promising, the limitations and occasional inaccuracies require validation by healthcare professionals. This review also examined global chatbot adoption in healthcare, emphasizing the need for user awareness to ensure patient safety. Chatbot technology holds great promise in OBGYN and medicine, offering innovative solutions while necessitating responsible integration to ensure patient care and safety.

Indexed as

Artificial intelligenceChatbotsDeep learningNatural language processingObstetrics

Identifiers

PMID38247132
PMCPMC10948210

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.