Prediction of maternal and infant outcomes with a Mother-Child AI agent

Researchers developed the Mother-Child AI Agent (MoChiAgent), an LLM-based clinical assistant, which uses its predictive engine MoChiFormer to forecast maternal and infant diseases from electronic health records, achieving AUROCs of 0.89 for placental abruption, 0.89 for premature rupture of membranes, and 0.91 for preterm labour. The model was internally evaluated on 4,401,599 longitudinal clinical visits and externally validated on 263,452 maternal and 23,192 infant visits, and identified transgenerational risks such as elevated risks of neonatal jaundice (HR = 2.81) and haematological diseases (HR = 2.83) in infants of high-risk mothers.

Abstract Current predictive models for pregnancy and infant outcomes often focus on limited endpoints and rely on costly tests or imaging. Here we developed the Mother-Child AI Agent MoChiAgent , an LLM-based clinical assistant that orchestrates multiple tools to integrate sequential electronic health record EHR data, including routine laboratory tests, for forecasting maternal and infant diseases. MoChiAgent’s core predictive engine, MoChiFormer, was developed and internally evaluated using 4,401,599 longitudinal clinical visits and externally validated using independent maternal and infant cohorts consisting of 263,452 and 23,192 visits, respectively. MoChiFormer reconstructs missing laboratory values, reduces batch effects and learns EHR representations that support gestational, fetal and infant age estimation, health-trajectory modelling and stratification of current and future disease risk. Subsequently, a Knowledge Search Tool utilizes these forecasts to retrieve evidence-based intervention and treatment recommendations from curated medical literature and authoritative guidelines. For maternal health, MoChiFormer accurately identified key gestational conditions, achieving AUROCs of 0.89 for placental abruption, 0.89 for premature rupture of membranes, and 0.91 for preterm labour. Analysis of paired mother-infant data further revealed transgenerational risk associations, with infants born to mothers in specific clusters showing substantially elevated risks of neonatal jaundice HR = 2.81, 95% CI 2.60-3.03 and haematological diseases HR = 2.83, 95% CI 2.62-3.05 . Integrating maternal gestational EHRs with infant records improved prediction of infant conditions, including chromosomal abnormalities and respiratory disorders. These findings suggest that MoChiAgent can provide clinically relevant, actionable decision-support information to enhance risk-stratified care for mothers and infants. Supplementary information Supplementary Information download PDF https://media.springernature.com/original/springer-static/esm/art%3A10.1038%2Fs41591-026-04694-y/MediaObjects/41591 2026 4694 MOESM1 ESM.pdf Supplementary Figures 1–11 and associated figure legends. Supplementary Tables download XLSX https://media.springernature.com/original/springer-static/esm/art%3A10.1038%2Fs41591-026-04694-y/MediaObjects/41591 2026 4694 MOESM3 ESM.xlsx Workbook containing Supplementary Tables 1–3: maternal high-versus low-risk cluster statistics for Figure 3b–i; infant transgenerational high-versus low-risk cluster statistics for Figure 5b–i; and abbreviations for clinical biomarkers and features used in Figure 2g–i. Source data Source Data Extended Data Fig. 7 download XLSX https://media.springernature.com/original/springer-static/esm/art%3A10.1038%2Fs41591-026-04694-y/MediaObjects/41591 2026 4694 MOESM4 ESM.xlsx Source data for Extended Data Fig. 7. This Excel file contains individual physician evaluation scores for each case and each system MoChiAgent, ChatGPT, Gemini and OpenEvidence across diagnostic accuracy, evidence traceability, completeness of the diagnostic and therapeutic plan, and clinical safety. Rights and permissions This article is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team mailto:journalpermissions@springernature.com . About this article Cite this article Liu, S., Zheng, W., Kang, J. et al. Prediction of maternal and infant outcomes from longitudinal electronic health records with a Mother-Child AI agent. Nat Med 2026 . https://doi.org/10.1038/s41591-026-04694-y - Received: - Accepted: - Published: - DOI: https://doi.org/10.1038/s41591-026-04694-y