Artificial intelligence has evolved exponentially from early expert rules to current generative AI models. The field of AI includes machine learning, deep learning, and generative AI and is shared between computer and data science. To understand artificial intelligence, one needs to grasp the rich history behind it and the evolution of machine learning as well. Deep learning with complex neural networks has been the most important advancement in AI and is only about a decade in existence. Convolutional neural networks (CNNs) are the backbone of image analysis and transformers are the backbone of modern natural language processing. AI was first impactful in medicine with image analysis in radiology but was followed by advances in cardiology and ophthalmology. Now, every medical specialty has seen AI applied to improve diagnostic accuracy and efficiency. The appearance of large language models in 2022 heralded a new period of innovation in medicine where they can be used for administrative tasks, differential diagnoses, chatbots for patients, and a myriad of other possibilities. Despite the unlimited potential of AI in medicine, significant challenges exist. Artificial intelligence can impact job security; it can be trained on biased data; it can be expensive to train and maintain; it can occasionally provide incorrect answers; there are privacy and security issues, and legal and regulatory issues. We can expect to see a combination of breathtaking technological advances in AI along with new challenges and potential solutions.