This capstone course gives you the opportunity to bring everything you have learned in the Informed Clinical Decision Making using Deep Learning Specialization together in one hands-on, practical project. You will work with real-world critical care data from the MIMIC-III database and tackle a clinically meaningful prediction task from start to finish.

You will choose one of three advanced projects focused on explainable artificial intelligence in healthcare: permutation feature importance, LIME, or Grad-CAM. Each project guides you through querying and preparing electronic health record data, building predictive models such as logistic regression or LSTM networks, and interpreting model predictions using state-of-the-art explainability techniques. The focus is not only on model performance, but on understanding and communicating why a model makes its predictions. By completing this capstone, you will gain practical experience translating deep learning models into insights that support trustworthy and transparent Clinical Decision Support Systems. This course is ideal for learners who want to demonstrate applied skills, build confidence working with clinical data, and showcase their ability to combine technical expertise with clinical reasoning.