During the past decade, deep learning has achieved great success in healthcare. However, most existing methods aim at model performance in terms of higher accuracy, which lacks the information reflecting the reliability of the prediction. It cannot be trustworthy for diagnosis making and even is disastrous for safety-critical clinical applications. How to build a reliable and robust healthcare system has become a focal topic in both academia and industry. In the talk, I will introduce our recent works for trustworthy AI in healthcare. Moreover, I also discuss some open challenges for trustworthy learning.