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
The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
Souren Pashangpour, Goldie Nejat
Interpretable Predictive Models for Healthcare via Rational Logistic Regression
Thiti Suttaket, L Vivek Harsha Vardhan, Stanley Kok
AI Readiness in Healthcare through Storytelling XAI
Akshat Dubey, Zewen Yang, Georges Hattab
Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare
Yifan Yang, Qiao Jin, Qingqing Zhu, Zhizheng Wang, Francisco Erramuspe Álvarez, Nicholas Wan, Benjamin Hou, Zhiyong Lu
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos, Yi Shen, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
Detecting Bias and Enhancing Diagnostic Accuracy in Large Language Models for Healthcare
Pardis Sadat Zahraei, Zahra Shakeri