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Am J Perinatol
DOI: 10.1055/a-2616-4182
Review Article

Application of Generative AI to Enhance Obstetrics and Gynecology Research

Tetsuya Kawakita 
1   Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, Virginia
,
Melissa S. Wong
2   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California
3   Division of Informatics, Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California
,
Kelly S. Gibson
4   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The MetroHealth System, Case Western Reserve University, Cleveland, Ohio
,
Megha Gupta
5   Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
6   Department of Obstetrics, Gynecology and Reproductive Biology, Harvard Medical School, Boston, Massachusetts
,
Alexis C. Gimovsky 
7   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Warren Alpert School of Brown University, Women & Infants Hospital of Rhode Island, Providence, Rhode Island
,
Hind N. Moussa
8   Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Cincinnati, Cincinnati, Ohio
9   Kettering Health Maternal Fetal Medicine, Kettering, Ohio
,
Heo J. Hye
10   Department of Obstetrics and Gynecology, NYU Grossman Long Island School of Medicine, Mineola, New York
11   Department of Health Informatics, NYU Langone Health, New York, New York
,
for the Society of Maternal-Fetal Medicine Clinical Informatics Committee› Author Affiliations
Funding None.
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Abstract

The rapid evolution of large-language models such as ChatGPT, Claude, and Gemini is reshaping the methodological landscape of obstetrics and gynecology (OBGYN) research. This narrative review provides a comprehensive account of generative AI capabilities, key use cases, and recommended safeguards for investigators. First, generative AI expedites hypothesis generation, enabling researchers to interrogate vast corpora and surface plausible, overlooked questions. Second, it streamlines systematic reviews by composing optimized search strings, screening titles and abstracts, and identifying full-text discrepancies. Third, AI assistants can draft reproducible analytic code, perform preliminary descriptive or inferential analyses, and create publication-ready tables and figures. Fourth, the models support scholarly writing by suggesting journal-specific headings, refining prose, harmonizing references, and translating technical content for multidisciplinary audiences. Fifth, they augment peer-review and editorial workflows by delivering evidence-focused critiques. In educational settings, these models can create adaptive curricula and interactive simulations for trainees, fostering digital literacy and evidence-based practice early in professional development among clinicians. Integration into clinical decision-support pipelines is also foreseeable, warranting proactive governance. Notwithstanding these opportunities, responsible use demands vigilant oversight. Large-language models occasionally fabricate citations or misinterpret domain-specific data (“hallucinations”), potentially propagating misinformation. Outputs are highly prompt-dependent, creating a reliance on informed prompt engineering that may disadvantage less technical clinicians. Moreover, uploading protected health information or copyrighted text raises privacy, security, and intellectual property concerns. We outline best-practice recommendations: maintain human verification of all AI-generated content; cross-validate references with primary databases; employ privacy-preserving, on-premises deployments for sensitive data; document prompts for reproducibility; and disclose AI involvement transparently. In summary, generative AI offers a powerful adjunct for OBGYN scientists by accelerating topic formulation, evidence synthesis, data analysis, manuscript preparation, and peer review. When coupled with rigorous oversight and ethical safeguards, these tools can enhance productivity without compromising scientific integrity. Future studies should quantify accuracy, bias, and downstream patient impact.

Key Points

  • Generative AI supports various research stages in OBGYN, such as hypothesis generation, systematic review assistance, data analysis, and scientific writing, demonstrating its potential to streamline research workflows and improve research efficiency.

  • Generative AI has notable limitations, including the risk of generating inaccurate references (“hallucinations”) and the need for careful supervision.

  • Effective usage requires skill in prompt engineering, posing a challenge for those without technical expertise.

  • Utilizing generative AI in sensitive fields like OBGYN raises privacy, security, and ethical concerns.

Keywords

artificial intelligence - chatGPT - Claude - Gemini - scientific research - review

Supplementary Material



Publication History

Received: 06 May 2025

Accepted: 19 May 2025

Accepted Manuscript online:
20 May 2025

Article published online:
11 June 2025

© 2025. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

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