The term “Artificial Intelligence” has come into such common usage, that it is now used interchangeably with any software which automates routine tasks. In fact, AI software at its current usage is not intelligent and cannot think out of the box. In this lecture, we will explore the history of software and hardware development, of pattern recognition algorithms and lately deep learning, which are expected to allow healthcare providers to perform their tasks with more reliability, accuracy, and to avoid critical errors, known in medicine as sentinel events. While there is excitement about robots and androids also performing human tasks, as physicians and healthcare providers, we cannot lose the human touch, as that is a critical component in improving the experience and probably outcome of patients when faced with medical illness. Currently deployed AI and healthcare software often fails by distracting providers and decreases human interaction time with patients. The current state of software also often fails in catching mistakes and may even cause mistakes. We will discuss what is needed in order to truly improve the patient’s experience and outcomes, and which tasks would ideally be taken over by a future “AI”. Real life case examples will be used to make the points in this lecture.