1. The document discusses explainable medical AI and techniques like Grad-CAM and SHAP that can help explain AI models in medical contexts like diabetic retinopathy detection and gene expression prediction. 2. It describes using Grad-CAM to help explain a model for detecting diabetic retinopathy and using SHAP to explain a gene expression prediction model by determining the contribution of different histone modification signals. 3. The author advocates that explainable AI is very important for medical AI to help ensure models are not seen as "black boxes" and to build trust in the use of AI in healthcare.