Abstract Visual Question Answering (VQA) is a significant cross-disciplinary issue in the
fields of computer vision and natural language processing that requires a computer to output …

Smart healthcare has achieved significant progress in recent years. Emerging artificial
intelligence (AI) technologies enable various smart applications across various healthcare …

Biomedical data is inherently multimodal, comprising physical measurements and natural
language narratives. A generalist biomedical AI model needs to simultaneously process …

Contrastive pretraining on parallel image-text data has attained great success in vision-
language processing (VLP), as exemplified by CLIP and related methods. However, prior …

Abstract Contrastive Language–Image Pre-training (CLIP) has shown remarkable success
in learning with cross-modal supervision from extensive amounts of image–text pairs …

Medical visual question answering (Med-VQA) has tremendous potential in healthcare.
However, the development of this technology is hindered by the lacking of publicly-available …

Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is
advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) …

Abstract Medical Visual Question Answering (VQA) is a combination of medical artificial
intelligence and popular VQA challenges. Given a medical image and a clinically relevant …

Generative models hold promise for revolutionizing medical education, robot-assisted
surgery, and data augmentation for machine learning. Despite progress in generating 2D …