Responsible AI for Multimodality Biomedical Data Analysis

The field of computational biology has focused on uni-modal medical data analysis for decades. However, given the inherent complexity of biomedical challenges, constructing computational approaches using multimodal datasets is critical to drive progress. In addition, improving the transparency and explainability of AI systems used to analyze biomedical data is vital. This necessity calls special attention to “explainable AI” (XAI) to interpret AI model black boxes and ensure responsible and ethical AI systems.

The focus of this full-day special session is integrating multimodality learning and AI model interpretability to gain a more nuanced understanding of complex biomedical problems and, in turn, improve patient care. We welcome scientists, industry leaders, educators and trainees to contribute to this session and share exciting findings on devising robust, responsible, and ethical AI systems.

For more information visit http://www.biodataxai.com/.