Explainable AI for Stratifying Drug-Related Fetal Risk in Pregnancy: A Model-Based Study

Pregnancy introduces a unique clinical dilemma in the management of psychiatric and neurological disorders, where ensuring maternal mental health must be carefully balanced against the potential risks to fetal development. Pharmacological treatments, while often necessary, carry varying degrees of teratogenic risk, particularly when administered during sensitive stages of gestation. In this study, we present an AI-based risk stratification framework that integrates machine learning (ML) and explainable artificial intelligence (XAI) techniques to quantify and interpret the likelihood of congenital malformations resulting from the use of psychiatric and neurological medications during pregnancy. We developed a synthetic yet clinically representative cohort of 1200 pregnant patients, incorporating a wide range of maternal, fetal, and pharmacologic features such as age, body mass index, gestational age, medication class, dosage, and trimester of exposure. Using a calibrated XGBoost classifier combined with isotonic regression and SMOTE oversampling, we achieved strong predictive performance with an area under the precision-recall curve (AUPRC) of 0.872 and an area under the receiver operating characteristic curve (AUROC) of 0.945. To ensure transparency and usability in clinical settings, we applied SHAP (SHapley Additive exPlanations) to elucidate feature contributions and developed five high-resolution visualizations, including a SHAP summary plot, risk histogram, stratified donut chart, boxplot of dosage by risk group, and a correlation heatmap. These figures provide a clear understanding of how individual risk factors contribute to outcome predictions. This study demonstrates that combining ML with XAI can produce an interpretable, scalable tool for risk stratification in perinatal psychiatry, enabling personalized decision-making and promoting safer pharmacological management during pregnancy.