Medical AI
Medical AI aims to improve healthcare through the development and application of artificial intelligence, focusing on enhancing diagnostic accuracy, treatment personalization, and overall efficiency. Current research emphasizes explainable AI models, fairness and bias mitigation in algorithms and datasets (often using techniques like federated learning and synthetic data generation), and the integration of large language models (LLMs) and multimodal data (including images, text, and physiological signals) for improved decision-making. These advancements hold significant potential to improve patient outcomes, streamline clinical workflows, and address healthcare disparities, particularly in resource-limited settings.
Papers
Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation
Imane Hamzaoui, Hadjer Benmeziane, Zayneb Cherif, Kaoutar El Maghraoui
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Mingyu Jin, Qinkai Yu, Dong Shu, Chong Zhang, Lizhou Fan, Wenyue Hua, Suiyuan Zhu, Yanda Meng, Zhenting Wang, Mengnan Du, Yongfeng Zhang
OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection
Fatema-E Jannat, Sina Gholami, Minhaj Nur Alam, Hamed Tabkhi
MINT: A wrapper to make multi-modal and multi-image AI models interactive
Jan Freyberg, Abhijit Guha Roy, Terry Spitz, Beverly Freeman, Mike Schaekermann, Patricia Strachan, Eva Schnider, Renee Wong, Dale R Webster, Alan Karthikesalingam, Yun Liu, Krishnamurthy Dvijotham, Umesh Telang