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When you think about the term 鈥渁rtificial intelligence鈥� or AI, maybe the first thing that comes to mind is robots or sci-fi movies. But AI is very much a part of our everyday lives and central to many industries today. Siri and Alexa are examples of AI that you鈥檝e likely come into contact with from your iPhone or Amazon device when making a hands-free call, searching for the number of teaspoons in half a tablespoon or to find out the age of a celebrity. But what if Siri and Alexa became your coworkers? While that might be a bit of an exaggeration (or at least very far off in the future), AI is already being used in healthcare settings and researchers are excited at the potential it has to improve care and efficiency. How can you, as a working nurse, expect to see AI show up in your field and how can you take advantage of it? Let鈥檚 get into it.

What is artificial intelligence?

On a macro scale, IBM explains that artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.¹ But AI can be broken up into two main subfields: deep learning and machine learning.

Deep learning uses artificial neural networks鈥� algorithms that are trained (by a human)鈥� to mimic the way a human brain learns from large amounts of data in order to perform tasks. Deep learning is very often used for automated tasks. Auto-generated captions on YouTube and facial recognition to unlock your phone or for identifying and grouping photos of friends in your digital photo galleries are examples of deep learning.²

Machine learning is focused on improving performance of a technology by taking in new data. Think about how the more you use and browse a music or video streaming service and rate things you do and don鈥檛 like, the better your recommendations are. Machine learning is also often used to forecast and make predictions. For example, a device that uses machine learning could monitor the function of a power grid and send a warning to the operators of the grid if it鈥檚 at risk for malfunction or failure.³

Smart home devices, mobile map applications, digital ads, online banking, and countless other things and businesses you use every day use AI in some way. And the world of healthcare is no different. Artificial intelligence in nursing is just on the precipice of making its full impact.

Examples of artificial intelligence in nursing and healthcare

Something the healthcare industry has a lot of is data. That trove of data is also what makes healthcare a prime candidate for digging into the full potential of machine and deep learning. Here is a sampling of the ways AI is already being put to use to improve care:

• Data collection through wearable devices like smart watches, inpatient bedside sensors, heart monitors and more can collect data and in real time warn the patient, or a member of their medical team, if something isn鈥檛 right. The collected data can also be used by the care providers to monitor symptoms and behaviors. Similarly, using these devices can help patients take care of themselves better when outside of a care facility by sending reminders to exercise, drink water, check their blood pressure and more. Voice-recognition and short form writing programs can also help nurses and clinicians take notes more effectively while also giving the patient their full attention.
• Early detection in the form of predictive analytics is used all across the healthcare spectrum to forecast trends in public health issues, chronic health conditions and the likelihood of patients to develop complications like sepsis during a hospital stay.
• Medical training and education also benefit from artificial intelligence. Students can be put into incredibly realistic simulations where AI-powered 鈥減atients鈥� in the forms of manikins or computer programs present symptoms or behavior that require the learner to respond with the right action or response.
• Clinical decision support is another great example of using AI to improve healthcare. By using all the data it can find within an electronic health record (EHR) system or intake notes, sensors and more, AI can help nurses and other healthcare professionals make decisions quickly and confidently.

While you might read through some of these uses and think 鈥渁ren鈥檛 nurses supposed to do that?鈥� AI systems can simply process much more data more quickly than a human is able to. It might also see something that a busy human would miss! Using artificial intelligence in nursing is a clear solution to many different issues in the field and should be seen as an exciting opportunity to provide an even higher level of care.

How artificial intelligence could transform healthcare

The current capabilities of artificial intelligence in nursing and healthcare are remarkable but the proposed uses (some of which are close to reality!) are phenomenal.⁴

• Brain-computer interfaces (BCI): In the situation where a person loses their ability to communicate verbally and physically, BCI could be completely life-changing. By charting the neural activities associated with the intended movement of one鈥檚 hand, BCI could then send messages through a device like an iPad. This type of technology could come in key for individuals with ALS, a patient who has suffered a stroke, is experiencing locked-in syndrome or other neurological trauma.
• Advanced radiology technology: Just like AI is currently used to identify faces or particular objects in photos, the hope is that one day it could similarly be used to look at radiology images captured by MRI machines, CT scanners, and x-rays. Not only could this speed up diagnoses but it could also help prevent more invasive diagnostic tests like biopsies or exploratory surgery. This sort of capability would also help underserved areas where they may be understaffed in radiologists and other clinicians.
• Quicker, more precise pathology: Approximately 70% of care decisions are based on pathology results. Using AI to interpret data from notes, tests, and images, diagnoses can be made more quickly. Plus, technology can now drill down to the pixel level on extremely large digital images which can allow providers to identify nuances that may escape the human eye.

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