{
  "abstract": "IMPORTANCE: Generative artificial intelligence (AI) chatbots are increasingly used to support maternal health, but randomized evidence is limited.\n\nOBJECTIVE: To evaluate whether access to a generative AI maternal health chatbot improved 12-month maternal health outcomes.\n\nDESIGN, SETTING, AND PARTICIPANTS: This 2-group randomized clinical trial enrolled 400 participants from October 2023 to December 2024, with 12-month follow-up completed in December 2025. Participants were recruited online across 49 US states and the District of Columbia. Eligible participants were English-speaking, aged 14 years or older, pregnant or parenting an infant younger than 6 months, and self-identified as members of racial and/or ethnic minoritized groups.\n\nINTERVENTIONS: Participants were randomized 1:1 to receive access to the chatbot or an active control consisting of monthly children’s books.\n\nMAIN OUTCOMES and MEASURES: Primary outcomes were anxiety and depression symptoms at 12 months measured using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9. Secondary outcomes included emergency department visits, pregnancy-related complications, postpartum care checkups, effective contraception use, and sleep duration.\n\nRESULTS: Among 400 randomized participants, the mean (SD) age was 32.6 (5.1) years; 200 (50%) were parenting an infant at enrollment; and 10 (2%) identified as American Indian or Alaska Native, 80 (20%) Asian, 188 (47%) Black or African American, 57 (14%) multiracial, and 65 (16%) Hispanic or Latino. The AI chatbot was not associated with differences in anxiety scores (adjusted mean difference, 0.10; 95% CI, −0.77 to 0.96) or depression scores (adjusted mean difference, −0.23; 95% CI, −1.05 to 0.59), nor with emergency department visits, postpartum care, contraception use, or sleep duration. Among participants pregnant at enrollment, the chatbot was associated with more self-reported pregnancy-related complications in the secondary 12-month end point analysis, but not in longitudinal analyses. Higher chatbot engagement was associated with longer sleep duration and higher anxiety scores in exploratory within-group analyses.\n\nCONCLUSIONS AND RELEVANCE: In this randomized clinical trial of pregnant and postpartum individuals from racial and ethnic minoritized groups, access to a generative artificial intelligence maternal health chatbot did not improve maternal anxiety or depression at 12 months compared with an active control. Secondary and exploratory findings should be interpreted cautiously.\n\nTRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06053515",
  "abstract_shown": true,
  "affiliations": [
    "Department of Behavioral and Community Health, University of Maryland School of Public Health, College Park",
    "Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park",
    "Department of Computer Science, UMIACS, University of Maryland, College Park",
    "National Institute of Nursing Research (NINR), National Institutes of Health (NIH), Bethesda, Maryland"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Elizabeth M. Norell"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaohe Yue"
    },
    {
      "affs": [
        1
      ],
      "name": "Heran Mane"
    },
    {
      "affs": [
        0
      ],
      "name": "Francia Ximena Marin Gutierrez"
    },
    {
      "affs": [
        2
      ],
      "name": "Neha Pundlik Srikanth"
    },
    {
      "affs": [
        3
      ],
      "name": "Daniel Smolyak"
    },
    {
      "affs": [
        3
      ],
      "name": "Pankaj Dipankar"
    },
    {
      "affs": [
        0
      ],
      "name": "Carson J. Peters"
    },
    {
      "affs": [
        2
      ],
      "name": "Jordan Boyd-Graber"
    },
    {
      "affs": [
        1
      ],
      "name": "Thu T. Nguyen"
    },
    {
      "affs": [
        1
      ],
      "name": "Xin He"
    },
    {
      "affs": [
        3
      ],
      "name": "Quynh C. Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ggbzsshx3v2g.bib",
  "cited_by": 0,
  "doi": "10.1001/jamanetworkopen.2026.41062",
  "fulltext_url": null,
  "id": "3ggbzsshx3v2g",
  "issns": [
    "2574-3805"
  ],
  "landing_url": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2856248",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "American Medical Association (AMA)",
  "ris": "https://research.draconitic.com/paper/3ggbzsshx3v2g.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:07Z",
      "native_id": "10.1001/jamanetworkopen.2026.41062",
      "source": "crossref",
      "url": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2856248"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2574-3805",
      "subfield": "general"
    }
  ],
  "title": "Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and Postpartum Individuals",
  "title_text": "Generative Artificial Intelligence and Maternal Health Outcomes in Pregnant and Postpartum Individuals",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:21:22Z",
  "url": "https://research.draconitic.com/paper/3ggbzsshx3v2g",
  "venue": "JAMA Network Open",
  "versions": []
}