The use of deep learning in medicine is beginning to have an impact upon clinicians, healthcare systems and patients. The journey has just begun.

1. In the near future, almost every type of clinician, ranging from specialty doctor to paramedic, will be using AI technology, and in particular deep learning.
2. Using electronic health record data, machine- and deep-learning algorithms are already predicting many important clinical parameters.
3. The goal for patients is to develop deep-learning algorithms to support them to take their health care into their own hands.

AI is bringing a paradigm shift to healthcare, powered by the increasing availability of healthcare data and the rapid progress of analytic techniques. 

1. The increasing availability of healthcare data and rapid development of big data analytic methods have made possible the recent successful application of AI in healthcare.               
2. AI can be applied to various types of healthcare data in the form of Machine Learning (structured, imaging, genetic and EP data) and natural language processing (unstructured data).                   
3. Regulatory process and data exchange are the main obstacles for real-life implementation.

Machine Learning can reveal previously unknown associations, generate novel hypotheses and drive researchers and resources towards more productive channels of enquiry.

Healthcare providers should be prepared for the age of AI and embrace the added capabilities that will lead to more efficient and effective care.

1. Early diagnosis can now be achieved for many conditions by improving the extraction of clinical insight and feeding such insight into a well-trained and validated system.
2. Integrating machine learning-based modeling can facilitate the detection of potential complications, improve healthcare resource utilization and outcomes at a personalized level.
3. Artificial Intelligence analytics can be used in chronic disease management characterized by multi-organ involvement, acute variable events and the progression of long term illness.

An AI system can help to rapidly diagnose COVID-19 patients.

1. Rapid detection of patients with COVID-19 is vital because an initial false negative could delay treatment and increase risk of viral transmission to others.
2. With a higher AUC, the joint model integrating CT images and associated clinical information outperformed the model trained on CT images only.
3. The AI system could be implemented as a rapid diagnostic tool to flag patients with suspected COVID-19 infection, when CT images and/or clinical information are available, and radiologists could review these suspected cases identified by AI with a higher priority.