What are the Odds? Utilization of Artificial Intelligence to Predict Success in the Integrated Plastic Surgery match

Background: Despite widespread consensus that quantifiable application characteristics impact match success, no prospective predictive algorithm has been created in any specialty since USMLE Step 1 Pass scoring. The use of artificial intelligence presents a novel method to combine these factors to predict success in the ever-competitive Integrated Plastic Surgery match. Methods: Quantifiable data from Plastic Surgery Common Applications (PSCAs) submitted to a single academic center in the 2023-2024 and 2024-2025 match cycles were collected. 70% of applications from the 2023-2024 cycle were utilized to train a random-forest classification model to predict match status. This model was subsequently evaluated using the remaining 30% of 2023-2024 applicants and prospectively validated with applicants from the 2024-2025 match cycle. Results: The single most important predictor of match rate in our model was graduation from a highly ranked medical school, followed by average recommendation letter strength and USMLE Step 2 score (Figure 1). The predictive model demonstrated strong performance in classifying matched and unmatched cases in the 2023-2024 data with an AUROC of 0.85 and a balanced accuracy of 82.7% (95% CI 73.7-89.6%, p<0.0001). Prospective validation yielded consistent results, with an AUROC of 0.84 and a balanced accuracy of 77.6% (95% CI 73.1-81.7, p<0.0001), validating the longitudinal accuracy of this model (Figure 2). The final model was deployed within the Match Prediction App, allowing real-time probability estimation of match outcomes based on selected input features (Figure 3). Conclusions: This model provides a comprehensive analysis of residency match predictors, aiding both applicants and program directors in understanding key factors that influence match outcomes. While academic metrics like Step 2 scores are critical, the significance of letters of recommendation highlights that holistic review remains essential for residency selection.