Artificial intelligence is not one technology but rather a collection of them. Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely. Some particular AI technologies of high importance to healthcare include natural language processing, rule-based expert systems, and physical robots.

Making sense of human language has been a goal of AI researches since the 1950s. The field of natural language processing includes applications such as speech recognition, text analysis, and translation. There are two basic approaches to it: statistical and semantic NLP. Statistical is based on machine learning and has contributed to a recent increase in the accuracy of recognition. In healthcare, the applications of NLP involve the creation, understanding, and classification of clinical documentation in published research. NLP systems can analyze unstructured clinical notes on patients and prepare reports which will greatly improve the healthcare system and accuracy of the clinical information passed.

In terms of rule-based expert systems, these require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain. The 'if-then' rules were the dominant technology for AI in the 1980s and were widely used commercially in that and later periods. This means that there is a chance that AI can be integrated into healthcare from that perspective as well. However, when the number of rules is large and the rules begin to conflict with each other, they tend to break down. Yet, this can be prevented with many electronic health record (EHR) providers who can furnish a set of rules with systems today.

Furthermore, physical robots (a well-known phenomenon) are a part of digital technology that can be easily added, given that more than 200,000 industrial robots are installed each year around the world. They perform pre-defined tasks like lifting, repositioning, welding or assembling objects in places like factories and warehouses. These physical robots seem to already be well integrated into the health care system by delivering supplies to hospitals., However, as they seem to be becoming more intelligent, more work is requiring their capabilities. An idea of using surgical robots could provide almost 'superpower' qualities to surgeons, improving their ability to see and create precise and minimally invasive incisions as well as stitch wounds. The list of locations where physical robots could be implemented just seems to carry on. 

In conclusion, due to the complexity and rise of data in healthcare, Artificial Intelligence has a chance of being increasingly applied within the field. Several types of AI are already being employed by payers and providers of care and life sciences companies. However, with these three key added components of NLP, rule-based expert systems and physical robots, the future of healthcare will be completely revolutionized showing the great potential for AI in healthcare.