Application of Generative AI to Enhance Obstetrics and Gynecology Research

Clinical Snapshot

Key Findings

• P Value: Not applicable for narrative review

• Effect Size: Not quantified - qualitative assessment only

• Primary Outcome: Five key applications identified: hypothesis generation, systematic review assistance, data analysis, scholarly writing, and peer review support

• Nnt Or Sensitivity: Not applicable - no diagnostic or therapeutic intervention studied

• Confidence Interval: Not applicable for narrative review

Clinical Application

High feasibility with appropriate training and infrastructure, though requires technical literacy Relevant to Australian OBGYN research community; considerations needed for TGA regulations on AI in healthcare, privacy laws, and NHMRC research guidelines OBGYN researchers, clinicians, and trainees in academic and clinical settings

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. · 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..

References

1. 1.Kawakita, T., Wong, M. S., Gibson, K. S., Gupta, M., Gimovsky, A. C., Moussa, H. N., & Hye, H. J. (2025). Application of Generative AI to Enhance Obstetrics and Gynecology Research. American Journal of Perinatology. https://doi.org/10.1055/a-2616-4182