{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:18:31Z","timestamp":1791893911290,"version":"4.3.4"},"reference-count":53,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J Gynecology &amp;amp; Obste"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long\u2010term health risks for mothers and offspring. Multiple micronutrient supplementation (MMS) during pregnancy has demonstrated benefits, including reduced risks of low birth weight, small\u2010for\u2010gestational\u2010age births, and neonatal mortality, when compared with standard iron\u2013folic acid supplementation. Current MMS strategies, however, often follow a standard MMS, overlooking variations in nutritional status, health profiles, and context. Advances in artificial intelligence (AI), particularly deep learning and natural language processing, provide opportunities to strengthen maternal nutrition programs by integrating diverse data sources. Rather than promising fully individualized recommendations, AI could help stratify women by risk of insufficiencies or deficiencies, highlight groups most likely to benefit from additional support, and inform the design of more responsive supplementation strategies during preconception and pregnancy. We outline a conceptual model in which multimodal health data\u2014including electronic health records (EHRs), wearable sensor outputs, nutrition and fertility app logs, genomic markers, and sociodemographic information\u2014are aggregated and analyzed by AI systems to inform personalized MMS plans. The framework introduces the concept of a \u201cnutritional digital twin,\u201d a virtual profile of the patient's nutritional and metabolic state. This digital twin can simulate micronutrient needs and predict maternal\u2013fetal outcomes under different supplementation scenarios, enabling clinicians to test scenario\u2010based options (e.g. standard MMS\u2009\u00b1\u2009targeted add\u2010ons) for individuals. We describe how deep learning models can identify complex patterns (e.g. diet\u2013genome interactions or behavioral trends) while natural language processing (NLP) algorithms extract clinically relevant insights from unstructured data (such as medical notes or patient queries). In addition, we discuss the role of digital maternal health tools, such as mobile apps and wearable trackers, in supplying real\u2010time data to the AI models and in engaging women to improve adherence to supplementation regimens. Harnessing AI for MMS could transform maternal nutrition care in both high\u2010 and low\u2010resource settings. In high\u2010income contexts, rich data (comprehensive EHRs, genetic tests, continuous monitoring devices) could feed advanced predictive models to support risk\u2010stratified care with protocolized supplementation options, under clinical oversight. In low\u2010 and middle\u2010income countries, where maternal undernutrition and micronutrient gaps are most prevalent, AI\u2010driven approaches can help stratify risk groups and optimize limited resources. Ubiquitous mobile phone access and digital health tools in many such settings provide avenues for data collection and intervention delivery. We highlight examples where machine learning on population data revealed \u201chidden hunger\u201d patterns and key predictors of low supplement uptake (e.g. low education, minimal antenatal visits)\u2014insights that policymakers can use to target nutrition programs. A nutritional digital twin could further allow scenario\u2010testing (e.g. predicting the impact of adding a vitamin D supplement for a specific patient) before clinical decisions are made. To realize this vision, the key concerns are ethics, credibility, and fairness. Ethical frameworks must guide development so that sensitive reproductive health data are protected and clinician oversight remains central. The credibility of AI\u2010generated recommendations depends on transparency about the assumptions used to translate nutritional and health data into supplement type and dose, and on prospective validation against maternal and neonatal outcomes. This requires a continuous feedback loop in which recommendations are tested in real\u2010world settings and recalibrated using outcomes data, ensuring that the system learns from observed benefits and harms, rather than relying solely on theoretical modeling. Fairness demands that training data sets represent diverse populations and that solutions are tailored to local contexts to reduce bias and avoid widening disparities. Critically, the approach must be fed by data streams that extend beyond initial demographics and clinical baselines to include biomarkers, adherence patterns, and pregnancy outcomes, so that the models can be refined and dosing rules adjusted over time. If these safeguards are embedded, AI\u2010enhanced personalized MMS can move beyond proof of concept towards a credible, equitable, and empirically grounded contribution to global maternal health. AI\u2010driven personalized nutrition support represents a frontier in obstetric care. By combining clinical knowledge with data\u2010driven intelligence, we can move beyond generalized prenatal supplements towards precision maternal nutrition. The integration of deep learning models and digital health innovations into antenatal care pathways has the potential to better nourish pregnancies, save lives, and ensure healthier futures for mothers and children worldwide.<\/jats:p>","DOI":"10.1002\/ijgo.70911","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T19:47:52Z","timestamp":1772999272000},"page":"1163-1173","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence for personalized multiple micronutrient supplementation in maternal health"],"prefix":"10.1002","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4070-4814","authenticated-orcid":false,"given":"Gabriel","family":"Davis Jones","sequence":"first","affiliation":[{"name":"Oxford Digital Health Labs, Nuffield Department of Women's and Reproductive Health University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aris T.","family":"Papageorghiou","sequence":"additional","affiliation":[{"name":"Nuffield Department of Women's and Reproductive Health University of Oxford  Oxford UK"},{"name":"Oxford Maternal and Perinatal Health Institute (OMPHI) University of Oxford  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Shehata","sequence":"additional","affiliation":[{"name":"Royal College of Obstetricians and Gynaecologists  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hema","family":"Divakar","sequence":"additional","affiliation":[{"name":"Divakars Speciality Hospital  Bengaluru India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eline M.","family":"Van der Beek","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University Medical Centre Groningen University of Groningen  Groningen the Netherlands"},{"name":"Nestl\u00e9 Institute of Health Sciences, Nestl\u00e9 Research  Lausanne Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vyta","family":"Senikas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine McGill University  Montreal Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin C.","family":"Konje","sequence":"additional","affiliation":[{"name":"Feto Maternal Centre  Doha Qatar"},{"name":"Department of Obstetrics and Gynecology, Weill Cornell Medicine  Doha Qatar"},{"name":"Department of Health Sciences University of Leicester  Leicester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne\u2010Beatrice","family":"Kihara","sequence":"additional","affiliation":[{"name":"Department of Obstetrics &amp; Gynaecology University of Nairobi  Nairobi Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moshe","family":"Hod","sequence":"additional","affiliation":[{"name":"Sackler Faculty of Medicine Tel Aviv University  Tel\u2010Aviv Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the FIGO Committee on Women's Health and Technology","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpeds.2016.05.013"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0029665121000902"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrendo.2016.37"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcn\/81.5.1206"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)60996-4"},{"key":"e_1_2_13_7_1","article-title":"Multiple\u2010micronutrient supplementation for women during pregnancy","volume":"4","author":"Haider BA","year":"2017","journal-title":"Cochrane Database Syst Rev"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(17)30371-6"},{"key":"e_1_2_13_9_1","article-title":"Multiple\u2010micronutrient supplementation for women during pregnancy","volume":"3","author":"Keats EC","year":"2019","journal-title":"Cochrane Database Syst Rev"},{"key":"e_1_2_13_10_1","volume-title":"Guideline: Daily Iron and Folic Acid Supplementation in Pregnant Women","author":"World Health Organization","year":"2012"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-025-22129-0"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/mcn.12704"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/nutrit\/nuab004"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/jn\/133.5.1661S"},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/cga.12232"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-20685-5"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41110-025-00360-4"},{"key":"e_1_2_13_18_1","unstructured":"TsaiC\u2010H.Generating Personalized Pregnancy Nutrition Recommendations with GPT\u2010Powered AI Chatbot. 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