In this research paper, Zhao et al. constructed an early intervention model to predict rupture risk in ectopic pregnancy (EP). Ruptures in EP remain one of the leading causes of maternal death in the first trimester, especially in low-resource areas, and pose a challenge to treat when diagnosis is delayed. Previous studies found ways to address this issue using predictors such as abdominal pain, ß-hCG, progesterone, and ultrasound features, however, a prediction model integrating all these variables was lacking. The authors decided to bridge this gap by creating a web-based dynamic nomogram using statistical analyses and machine learning. From June 2019 to June 2022, they analyzed 543 EP cases (58 ruptures) from Hexian Memorial Affiliated Hospital of the Southern Medical University, (Guangzhou, China) and randomly divided the cases into two groups: 70% in the training cohort and 30% in the validation cohort. Variable screening (LASSO, stepwise regression, and optimal subset regression) and XGBoost chose and ranked the best clinical/ultrasound predictors. Two preliminary prediction models were produced: Model A with six variables (mass location, longest diameter, mass border, RPE, PE, and ascites) and Model B with the same six variables and ß-hCG. Model B (AUC = 0.94) performed slightly better than Model A (AUC = 0.92), and SHAP found that ß-hCG was the highest contributor, so Model B was chosen as the final model. Model B was further validated and found to have excellent discrimination (Training: AUC = 0.941; Validation: AUC = 0.970), meaning the model correctly identified rupture cases 94-97% of the time, and good calibration (Training: Brier = 0.044, Validation: Brier = 0.034; H-L: p = 0.053 and 0.756), meaning there was no over- or underestimate of rupture risks. Decision-curve analysis and the clinical impact curve showed net clinical benefit across 1-94.82% risk thresholds, meaning there was clinical significance in helping doctors decide when to give patients surgical treatment before rupture occurred. The authors turned this robust prediction model to a dynamic (Shiny-app) web-based nomogram. This program will help translate complex machine learning predictions into information physicians can utilize and understand, such as individualized treatment decisions, identifying high-risk patients in need of surgery, while also avoiding misdiagnosis. Even though there are some limitations with the study: sample size, the need for multicenter trials, and no assessment of recurrence or long-term follow ups, the authors showed the potential for AI prediction models to provide early intervention and improve maternal health.