Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

The article presents a novel clinical decision support AI system that integrates Treatment Effect estimation, a patient Digital Twin for simulating treatment trajectories, and Reinforcement Learning for adaptive decision-making. It operates through a continuous learning loop based on historical medical records and incorporates a rule-based safety module to monitor vital signs, ensuring safe treatment recommendations. The framework was validated using both synthetic simulations and a real-world ovarian cancer dataset, showing improved effectiveness and stability over standard methods, highlighting its potential for personalized medicine in clinical settings.