In recent years, advances in artificial intelligence techniques haveyielded immense success in computer vision, natural language processing, and speech processing. Healthcare is also one of the areas which got much benefited through this. Mining social media messages for health and drug-related information has received significant interest in pharmacovigilance research. For instance, an analysis of social media text (e.g., tweets, posts, and comments) using natural language processing and machine learning techniques helps in finding the adverse drug reactions, suicidal ideation, depression detection, medical information extraction, etc. Moreover, computer vision and machine learning techniques help the automatic detection of different disease from tissue images. For instance, it has shown immense success in the detection of cancer, diabetes, kidney failure, etc. Furthermore, speech processing in conjunction with artificial intelligence has shown great success in the treatment of people. Moreover, artificial intelligence helps in building systems for people with different abilities. At MIDAS@IIITD, we focus on several such interesting research problems (e.g., kidney glomeruli classification, automatic kidney fibrosis assessment, adverse drug reactions, and suicidal ideation ) leveraging deep learning techniques. Our recent papers in this area are published in top-tier conferences and journals such as IEEE Intelligent Systems, NAACL, etc.