Artificial intelligence in healthcare

Artificial intelligence in healthcare involves the use of complex algorithms and software to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input.
What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. This can be done through machine learning algorithms and deep learning which can recognize patterns in behavior and create their own sense. To reduce the margin of error, AI algorithms need to be tested repeatedly. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can’t adjust itself and only understand what it has been told explicitly, (2) and some deep learning algorithms are black boxes; algorithms can predict extremely precise, but not the cause or the why.
The primary aim of health-related AI applications is to analyze connections between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and the British National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, and Microsoft have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI software to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.


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