Artificial intelligence (AI) is widely used in healthcare to improve the efficiency and effectiveness of various administrative processes. This technology can help healthcare providers deliver better patient care and effectively manage their operations. Although AI is widely used in healthcare, its capabilities can vary depending on the organization and the tasks it supports. For instance, while some studies claim that it can perform better than humans in specific medical procedures, it will be several years before AI completely replaces humans in healthcare.
It still needs to be clarified what it can do and how it will affect the industry’s future. For instance, how will it affect the operations of medical services and the quality of patient care? In this article, let’s talk about some of the various advantages of using AI in healthcare.
Machine Learning
Machine learning is a common type of AI used in healthcare. This broad technique is used in various aspects of the technology and healthcare industry. The most widely used application of machine learning in healthcare is precision medicine, which allows healthcare providers to predict the success of particular treatment procedures. This technology is instrumental in helping them improve the efficiency of their operations and manage their patients’ care. Most of the AI systems being used in healthcare require data for training.
Natural Language Processing
Over the years, the field of healthcare has been heavily influenced by the development of artificial intelligence. One of this technology’s most common applications is in classifying clinical documents. This technology can help healthcare professionals improve the efficiency of their operations by analyzing the data they collect.
Rule-Based Expert Systems
During the 80s and 90s, the use of expert systems that were based on variations of rules was rampant in the healthcare industry. Today, AI is widely used in healthcare to support the clinical decisions that healthcare providers make. Human engineers and experts in a specific knowledge area usually build these systems. They can function well up to a certain point and can even be easy to follow.
However, as the number of rules they create grows, they can start to conflict, eventually breaking down. If the knowledge area changes significantly, changing the rules can be very time-consuming and burdensome. Due to the emergence of machine learning in healthcare, the systems are now being replaced by approaches based on medical algorithms.