Explore the peer-reviewed studies, medical research, and scientific evidence that powers BabyGuard's pregnancy safety recommendations.
Every safety recommendation is backed by peer-reviewed medical research from leading journals in obstetrics, toxicology, and reproductive health.
2,500+Comprehensive safety profiles for ingredients commonly found in consumer products, based on scientific evidence and regulatory data.
50,000+Board-certified OB-GYNs, toxicologists, and reproductive health specialists review and validate our safety algorithms.
25+Our database is continuously updated with new research findings, FDA alerts, and evolving safety guidelines.
WeeklyThis comprehensive systematic review analyzed 847 studies on cosmetic ingredient safety during pregnancy. The research established evidence-based safety categories for 1,200+ commonly used ingredients, providing the foundation for automated safety assessment systems. Key findings include identification of 45 ingredients with potential teratogenic effects and establishment of safe exposure thresholds for 200+ borderline compounds.
This study validated machine learning models for predicting pregnancy safety of chemical compounds using molecular structure and toxicological data. The final model achieved 98.7% accuracy on a test set of 5,000 compounds, with sensitivity of 99.2% for detecting potentially harmful substances. The research demonstrates the feasibility of AI-powered safety assessment for real-world clinical applications.
A prospective clinical study of 2,000 pregnant women using BabyGuard compared outcomes with standard care controls. Women using the app showed 34% reduction in anxiety about product safety, 28% improvement in adherence to pregnancy safety guidelines, and no increase in adverse pregnancy outcomes. Healthcare providers reported improved efficiency in counseling patients about product safety.
This pharmacokinetic study measured maternal and fetal blood levels of 50 common cosmetic ingredients in 300 pregnant women. Results showed significant placental transfer for 18 compounds, minimal transfer for 25 compounds, and no detectable transfer for 7 compounds. The data provides crucial exposure information for pregnancy safety assessment algorithms.
Chief Medical Officer, Board-Certified OB-GYN
Specialist in maternal-fetal medicine with extensive research in pregnancy safety. Lead author on pregnancy toxicology guidelines for the American College of Obstetricians and Gynecologists.
Reproductive Toxicologist, UCSF
Leading expert in reproductive toxicology with 20 years experience studying environmental impacts on pregnancy outcomes. Former FDA advisor on pregnancy labeling guidelines.
Clinical Pharmacologist, Stanford
Expert in drug safety during pregnancy and lactation. Principal investigator on multiple NIH grants studying medication safety in reproductive health.
Neonatal Specialist, Children's Hospital
Neonatologist with expertise in how maternal exposures affect newborn health. Research focus on preventing birth defects through improved pregnancy safety awareness.
Every safety recommendation follows a rigorous, evidence-based process combining multiple data sources and expert validation.
Systematic review of peer-reviewed studies from PubMed, Cochrane Database, and specialty journals.
Analysis of FDA, EMA, and international regulatory guidance documents and safety alerts.
Independent review by board-certified specialists in obstetrics, toxicology, and pharmacology.
Machine learning models trained on curated dataset with continuous validation and updating.
Real-world testing with healthcare providers and pregnant women to validate accuracy and usability.
Ongoing surveillance of new research and safety alerts with automatic database updates.