Evidence map›Paper›PMID 39566919›Full record

ArticleObstetrics & gynecology science2025

Usefulness and limitations of Chat GPT in getting information on teratogenic drugs exposed in pregnancy.

Jung Yeol Han

Abstract read
In one paragraph

Article in Obstetrics & gynecology science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

1 author.

Jung Yeol HanKorean MotherSafe Counselling Center, Pregnancy & Breastfeeding Medicines Information Center, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a growing interest in using artificial intelligence (AI) technology to obtain information on the risk and safety of drugs in pregnancy. Inadvertent drug exposure in pregnant women is inevitable in acute or chronic diseases, especially in unplanned pregnancies. According to the Korean mother safe counselling center database (2010-2023), many Korean pregnant women and their families asked about the teratogenicity of their exposed drugs. The most frequently used drug was topiramate (n=2,018), followed by isotretinoin (n=1,972), dexamethasone (n=1,279), and doxycycline (n=1,119). Unexpectedly, thalidomide, a notorious teratogen that causes phocomelia, was included. It has been withdrawn from general prescriptions since 1961, except for the treatment of multiple myeloma. Her partner used it for the treatment of multiple myeloma. In this study, we evaluated the usefulness of AI Chat generative pretrained transformer (Chat GPT) by comparing information between AI and a literature review on isotretinoin, a well-known teratogen to which Korean pregnant women are frequently exposed. Chat GPT provides general information on teratogenicity for pregnant women and medical providers rather than on their exposure. Thus, AI can induce unnecessary termination of pregnancy due to misinformation and misperception in cases of notorious teratogens such as isotretinoin. Therefore, counseling on the teratogenicity of medication exposure in pregnancy must be performed with Chat GPT, as well as a literature review. Further studies are required to obtain more individualized information using AI in the field of teratology.

Indexed as

Chat GPTDrugPregnancyTeratogen

Identifiers

PMID39566919
PMCPMC11788689

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.