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Artificial intelligence🤖 in health care Welcome to world of Artificial intelligence.  the future Will lead by Artificial intelligence in all areas but today we going to see about ArtificialI Intelligence in health care so let's jump in to the topic . What is Artificial intelligence🤖 in health care? Artificial intelligence🤖 (AI) in healthcare is the use of algorithms and software to approximate human cognition in the analysis of complex medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input. Real life application of Artificial intelligence in health care 1.AI-assisted robotic surgery Let  imagine the future surgery are done by using AI🤖. Is this possible yes With an estimated value of $40 billion to healthcare, robots can analyze data from pre-op medical records to guide a surgeon’s instrument during surgery, which can lead to a 21% reduction in a patient’s hospital stay. Robot-assisted su

Page views Health tech The definition of Health Tech, or digital📺 health, is the use of technology (databases, applications, mobiles📱, wearables) to improve the delivery, payment, and/or consumption of care, with the ability to increase the development and commercialization of medicinal products. Some examples for health tech 1.GPS patches Miniature sensors embedded in the skin of patients have been tested since 2011 to monitor and locate disabled people and Alzheimer sufferers. 2.Communicating pens, telemonitoring and videoconference patients on dialysis French charity Calydial received the innovation prize at last year’s HIT (Health Information Technologies) fair for its telemedicine projects, which include monitoring patients with kidney failure. Calydial’s doctors can remotely monitor patients undergoing dialysis, live, thanks to a system of surveillance cameras set up by the patients’ bedside. 3. 3D Printing Originally used primarily in man

Types of Machine Learning Machine learning algorithms can be divided into three broad categories: Supervised learning : The computer is presented with example inputs and their desired outputs, and the goal is to learn a general rule that maps inputs to outputs. An example is an email spam filter. Unsupervised learning : No labels are given to the learning algorithm, leaving it on its own to find structure in its input (discovering hidden patterns in data). For example, imagine having data about all cars and their buyers. The system can find patterns and identify that, for example, people in the suburbs prefer SUVs with petrol engines, but people who live near to downtown, prefer smaller electrical cars. Knowing this can help the system predict who will buy which car. Reinforcement learning : A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The progra