This is a deep learning research project from 11-785 Introduction to Deep Learning at Carnegie Mellon University. We developed Adversarial Self-Correction (ASC), a framework that reduces toxic outputs in medical AI models through automated adversarial training. Our method achieved 9.1% toxicity reduction while maintaining model helpfulness, addressing the critical challenge of aligning specialized LLMs for safe deployment in high-stakes domains.