{
  "abstract": "Artificial intelligence (AI) is rapidly transforming healthcare, with obstetrics emerging as a field of particularly high potential. This review comprehensively synthesises the current landscape of AI applications in obstetrics, critically evaluating its benefits, challenges and future directions. We conducted a systematic literature search of articles published between January 2020 and July 2025 in the PubMed, Web of Science and IEEE Xplore databases. Our analysis reveals that AI is demonstrating significant utility across the field, revolutionising areas such as prenatal ultrasound diagnosis, electronic fetal monitoring and obstetric surgical assistance. Notably, some predictive models for pregnancy complications like pre-eclampsia have achieved an area under the curve (AUC) >0.9. Despite this promise, persistent challenges include data privacy concerns, a lack of model interpretability, algorithmic bias and unresolved medico-legal issues regarding liability. Ultimately, the successful translation of AI into clinical practice hinges on both technological refinements—such as multimodal data fusion and remote monitoring—and robust governance frameworks. Addressing these ethical, legal and translational hurdles through interdisciplinary collaboration is essential for the responsible integration of AI to improve global maternal and infant health outcomes.",
  "authors": [
    {
      "affiliations": [
        "Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China",
        "Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangzhou, China",
        "Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangzhou, China",
        "Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory Maternal-Fetal of Medicine, Guangzhou, China"
      ],
      "name": "Fang He"
    },
    {
      "affiliations": [
        "Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China",
        "Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangzhou, China",
        "Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangzhou, China",
        "Guangdong-HongKong-Macao Greater Bay Area Higher Education Joint Laboratory Maternal-Fetal of Medicine, Guangzhou, China"
      ],
      "name": "Dunjin Chen"
    }
  ],
  "title": "Expert review: current applications and future directions of artificial intelligence in obstetrics",
  "uid": "0767fdbe-509a-5397-84d2-10217848ded7"
}