Add Artificial Intelligence in Healthcare: how aI Shapes Medicine

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+<br>Healthcare sectors are increasingly adopting artificial intelligence to improve patient care and improve process efficiencies. Clearly, the use of AI in medicine has been expanding in the last few years. This is partly due to a desire by medical providers to expand their care offerings, and partly due to the maturing of artificial intelligence itself - AI has grown by leaps and bounds in the last couple years. At this point, AI in healthcare spans many of the core areas in medicine. AI shapes - to a lesser or greater degree - everything from diagnostics to health and [GlucoLife glucose wellness](https://schreinerei-leonhardt.de/glucolife-supplement-comprehensive-overview) to smart devices. In many ways, AI technology has become a "second layer" of healthcare provider. This is because AI software can adapt without human intervention, so it can "learn" to target human health needs on its own. Not surprisingly, many top top AI companies are cashing in on the trend.<br>
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+<br>With all of the investment and growth in AI technology, expect many more AI use cases for healthcare in the years ahead. Furthermore, companies can now use AI as a Service, or build their own intelligent apps using cloud-based AI services. Along with Big Data in healthcare, AI in healthcare is fast becoming a defining factor. Let’s look at what’s currently happening with AI in healthcare. Antibiotics, of course, help keep people healthy. However, their pervasive use is resulting in antibiotics-resistant bacteria that kills 70,000 people per year globally. Researchers use machine learning (an AI technique) to identify genes that cause antibiotic resistance in bacteria. AI is also being used to identify pre-symptomatic patterns in electronic healthcare records (EHRs) so more and earlier alerts can be sent to healthcare providers. Brain-computer interfaces are not a mainstream technology yet. However, there is a lot of interest in this area because brain-computer interfaces can replace other types of computer interfaces, which is particularly helpful for people with permanent or temporary disabilities.<br>
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+<br>For example, AI-enabled brain-computer interfaces can help stroke patients communicate with healthcare providers soon after a stroke rather than after rehabilitative therapy. AI has been used in cardiology for more than 20 years but its progress is slow, given the life and death consequences of heart conditions. An example of AI use is an implantable defibrillator that monitors the heart rhythms of patients at risk of a sudden heart attack. The device also [administers](https://www.dictionary.com/browse/administers) a shock if necessary. Over the longer term, data from wearables and implantables will be combined with Electronic Healthcare [Records](https://healthtian.com/?s=Records) (EHRs) for continuous patient monitoring so doctors have more current information about their patients. Developing nations have a different sent of problems than first world nations. First world nations are interested in ever more sophisticated forms of AI while developing nations are more concerned about providing basic services, including healthcare, to poor citizens and citizens living in remote areas.<br>
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+<br>Quite often, poverty and life in remote location go hand in hand. As a result, developing nations are using AI to provide healthcare access to those who would otherwise have no access to healthcare. Specifically, medical information pushed via a tablet to a member of the community who can read it and take appropriate action. The community representative can also use the tablet to take pictures of patients’ symptoms which the image recognition system compares with similar images to diagnose the condition. EHRs haven’t completely replaced paper yet, and even though their use is pervasive, receptionists, medical assistants and doctors must do a lot of manual entry. Here, voice recognition capabilities replace keyboards. So, instead of typing information into the system, the user can simply speak the information they want recorded in the EHR. Video-based image recognition capabilities will likely supplement EHRs in the future because it provides additional insight into patients’ conditions that AI is capable of analyzing, but humans may miss.<br>
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+<br>For example, image analysis systems can tell when a patient is lying about pain, which may indicate opiate-seeking behavior. More consumers are wearing health and  [GlucoLife blood sugar support](https://schreinerei-leonhardt.de/glucolife-supplement-comprehensive-overview) fitness bands or smart watches, though there are also medical grade devices that track even more information. Such devices, depending on their design and level of sophistication can provide insight into a person’s heart rate, oxygen level, [GlucoLife Blood Sugar](https://schreinerei-leonhardt.de/glucolife-supplement-comprehensive-overview) level, sleep patterns, breathing, gait, and more, providing healthcare providers with information they wouldn’t otherwise get between appointments. For example, a stroke patient’s recovery may show improvement based on the patient’s gait while the early signs of a heart attack could mean the difference between surgery and no surgery. AI recognizes patterns in the data to determine the current health status of a patient. Immunotherapy for cancer is not an exact science. While many immunotherapy options are available, a patient’s DNA determines whether the treatment will be effective. Since AI can analyze far more information far faster than humans, it’s capable of recognizing patterns in genetics strings and correlating those against immunotherapy options.<br>