The healthcare industry is constantly looking for innovative ways to improve quality, value, and results. Machine learning, a subset of artificial intelligence, is playing a key role in many healthcare innovations. From virtual biopsies to the management of patient records, machine learning is revolutionizing the healthcare industry. Natural language processing (NLP) is one type of machine learning that focuses on the computer's ability to understand, analyze, and generate human language.
It can be used to extract patient data from doctors' notes and interact with and communicate with the machine. Physical robots are also being used in healthcare applications. They can help surgeons during complex procedures that require precise movements. In many cases, robotic surgery reduces the invasiveness of the procedure, which can also reduce complications and improve outcomes.
Machine learning can also be used to detect patterns in large sets of data collected from electronic medical records (EHRs). This data can be used to develop new medical procedures, treat chronic diseases, and create doctor-friendly products. However, there are certain challenges associated with implementing AI and machine learning in healthcare. These include the quality of healthcare data, the need for a huge team of data experts, and the protection of confidential information.
Online courses such as Foundations of Machine Learning for Healthcare or AI in Healthcare offered by Stanford University can help you determine if this is your career path. With its potential to improve quality, value, and results in healthcare, machine learning is an exciting field to explore.
