The challenges of explainable AI in biomedical data science

With the surge of biomedical data science, more and more AI techniques are employed to discover knowledge, unveil latent data behavior, generate new insight, and seek optimal strategies in decision making. Different AI methods have been proposed and developed in almost all different biomedical data science fields that range from drug discovery, electronic medical records (EMRs) data automation, single-cell RNA sequencing, early disease diagnosis, COVID research, and healthcare analytics. The AI methods and systems also generate a massive amount of data or big data that not only bring unpreceded progress in biomedical fields but also new challenges for AI.