The article "Deep Learning for Healthcare: Review, Opportunities and Challenges" by Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T. Dudley, reviews the application of deep learning in healthcare. The authors highlight the challenges and opportunities in using deep learning to transform complex, high-dimensional, and heterogeneous biomedical data into actionable insights. They discuss the limitations of traditional data mining and statistical learning methods and emphasize the potential of deep learning to address these challenges. The article covers recent advancements in deep learning technologies and their applications in clinical imaging, electronic health records (EHRs), genomics, and wearable devices. It also addresses the need for improved interpretability and the integration of different data sources. The authors suggest that deep learning could be a powerful tool for advancing healthcare, but more research is needed to overcome challenges such as data volume, quality, temporality, domain complexity, and interpretability. They propose directions for future research, including feature enrichment and the development of holistic and meaningful interpretable architectures.The article "Deep Learning for Healthcare: Review, Opportunities and Challenges" by Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T. Dudley, reviews the application of deep learning in healthcare. The authors highlight the challenges and opportunities in using deep learning to transform complex, high-dimensional, and heterogeneous biomedical data into actionable insights. They discuss the limitations of traditional data mining and statistical learning methods and emphasize the potential of deep learning to address these challenges. The article covers recent advancements in deep learning technologies and their applications in clinical imaging, electronic health records (EHRs), genomics, and wearable devices. It also addresses the need for improved interpretability and the integration of different data sources. The authors suggest that deep learning could be a powerful tool for advancing healthcare, but more research is needed to overcome challenges such as data volume, quality, temporality, domain complexity, and interpretability. They propose directions for future research, including feature enrichment and the development of holistic and meaningful interpretable architectures.