Postdoctoral researcher Explainable AI and Medical Natural Language Processing

Providing high-quality explanation for AI predictions based on machine learning or deep learning is a challenging but valuable task. We develop a interactive framework, i.e., medical virtual assistant to provide human-readable and high-quality explanations for machine' medical decisions. The developed framework integrates multi-model data, external medical knowledge and question-answering systems to build up a easy-used, credible and powerful medical AI assistant for benefiting current medical systems. This funding project are ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine (ANTIDOTE) and AI powered Data Curation & Publishing Virtual Assistant (AIDAVA).