Clinical Artificial Intelligence
Clinical artificial intelligence (AI) focuses on developing and deploying AI systems to improve healthcare, primarily aiming to enhance diagnostic accuracy, treatment planning, and patient care. Current research emphasizes multimodal approaches integrating various data types (images, text, genomics) using architectures like large language models (LLMs), graph neural networks (GNNs), and foundation models, often incorporating techniques for uncertainty quantification and fairness mitigation. This field is significant due to its potential to improve patient outcomes, increase efficiency in healthcare delivery, and address existing biases and disparities in access to care, but rigorous evaluation and responsible development are crucial for successful implementation.
Papers
Large Language Models as Agents in the Clinic
Nikita Mehandru, Brenda Y. Miao, Eduardo Rodriguez Almaraz, Madhumita Sushil, Atul J. Butte, Ahmed Alaa
Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare
Juan M. García-Gómez, Vicent Blanes-Selva, José Carlos de Bartolomé Cenzano, Jaime Cebolla-Cornejo, Ascensión Doñate-Martínez