Integrating generative AI in perinatology: applications for literature review.

Abstract

Perinatology relies on continuous engagement with an expanding body of clinical literature, yet the volume and velocity of publications increasingly exceed the capacity of clinicians to keep pace. Generative artificial intelligence (GAI) tools - such as ChatGPT4, Claude AI, Gemini, and Perplexity AI - offer a novel approach to assist with literature retrieval, comparison of clinical guidelines, and manuscript drafting. This study evaluates the strengths and limitations of these tools in maternal-fetal medicine, using structured clinical prompts to simulate real-world applications. Perplexity AI demonstrated the best citation accuracy, while ChatGPT4 and Claude excelled in content summarization but required manual verification of citations. In simulated trials, GAI tools reduced the time to generate clinically relevant summaries by up to 70 % compared to traditional PubMed searches. However, risks such as hallucinated references and overreliance on machine-generated text persist. Use cases include summarizing aspirin use guidelines for preeclampsia and comparing ACOG vs. NICE protocols. GAI should be viewed as a supportive assistant, not a substitute, for expert review. To ensure responsible integration, clinicians must develop AI literacy, apply rigorous oversight, and adhere to ethical standards. When used judiciously, GAI can enhance efficiency, insight, and evidence-based decision-making in perinatal care.