Dr. Priya Mehtas team builds an AI model using 3,600 clinical notes. She splits data into training (70%), validation (20%), and test (10%). She then augments the training set by generating 50% more synthetic entries based on originals. How many total notes are in the augmented training set?

Wed, 15 Apr 2026 22:57:13 +0000

**How Dr. Priya Mehtas’ AI Model Transforms Clinical Data—A Glimpse into the Future of Healthcare Innovation** Across the United States, healthcare and technology are converging faster than ever, with artificial intelligence emerging as a pivotal force in reshaping clinical workflows and patient outcomes. A key driver of this shift is Dr. Priya Mehtas’ pioneering work on an AI model trained on 3,600 real clinical notes. By strategically splitting this dataset into structured components—70% for training, 20% for validation, and 10% for testing—her team has built a robust foundation for scalable, reliable insights. This rigorous approach not only strengthens the model’s accuracy but also reflects a growing industry desire for transparency and precision in AI-driven healthcare. Beyond its technical rigor, the team’s decision to augment the training set by generating 50% more synthetic entries based on original notes marks a thoughtful advancement in data utilization. This augmentation process preserves the clinical authenticity of the source material while expanding capacity for model learning. The result? A significantly enhanced training dataset—expanded both in scale and diversity—without compromising integrity or compliance. **How Much Do Synthetic Additions Boost the Training Set?**
The original clinical note corpus contains 3,600 entries. Splitting it into training (70% = 2,520 notes), validation (20% = 720 notes), and test (10% = 360 notes) ensures balanced performance evaluation. By generating 50% more synthetic entries derived directly from these originals, the team effectively increases the training base by 50%. A 50% increase on 2,520 means adding 1,260 synthetic notes.