Healthcare System
Healthcare systems are undergoing a digital transformation driven by the need to improve efficiency, equity, and patient outcomes. Current research focuses on leveraging machine learning, particularly deep learning models (like Residual Networks and Transformers) and large language models (LLMs), to analyze diverse data sources (electronic health records, medical images, wearable sensor data) for predictive analytics, personalized interventions, and improved diagnostics. This work emphasizes addressing biases and ensuring fairness in algorithms, as well as enhancing data privacy and security through techniques like federated learning and data-free quantization. The ultimate goal is to create more efficient, equitable, and effective healthcare delivery systems through data-driven insights and AI-powered tools.
Papers
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
David Oniani, Jordan Hilsman, Yifan Peng, COL, Ronald K. Poropatich, COL Jeremy C. Pamplin, LTC Gary L. Legault, Yanshan Wang
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti, Francesca Ieva, Matteo Matteucci