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Using AI in Healthcare

Introduction

Most of us are using IoT devices in our everyday lives, whether it be wearable fitness trackers or voice activated system such as Alexa or Google Home. However, applications are now infiltrating several industries, making them more efficient and effective whilst enhancing their societal benefit. One of the key industries is healthcare, so much so that it even has its own acronym, IoMT (Internet of Medical Things) to differentiate just how large the opportunity is. The potential of IoMT is huge as a world of managing previously unstructured data become easy through AI, machine learning, image and natural language processing. Doctors can tailor plans for specific patients based on all their historical data at the click of a button, avoiding oversight and potential complications. In healthcare, IoT could literally be lifesaving.

Applications of Artificial Intelligence

As we know, IoT technology is fuelled by data. It is important to keep re-iterating that the device is really just a box to collect and store that data via sensors and the AI applications take that to process it and make decisions. By 2020, it is thought that healthcare providers and organisations will spend an average of $54 million on artificial intelligence projects. There has even been some discussion as to whether human physicians could be replaced by machines and whilst this isn’t really feasible for ethical reasons beyond anything else, AI will definitely become a highly skilled assistant in clinical decision making.

Training the AI

Before IoT devices can be used within healthcare, they need to be trained using existing data. These devices learn from experience to analyse the correlations between subjects, symptoms and decisions. For example, in July 2018, researchers in Japan ran a successful experiment in training AI to detect stomach cancer. This was done on a relatively small scale, loading the device with 100 early-stage cancer images and 100 normal stomach tissue images so it could learn the patterns. Through this training data, the AI took just 0.004 seconds to detect the images with early stage symptoms to an 80% accuracy and to a 95% accuracy for those with normal symptoms. It is thought that these early signs are incredibly difficult to detect and often misconstrued as inflammation by doctors, meaning this AI made quite an amazing breakthrough. Just imagine if this device had 1,000 or 1,000,000 images to work with and the potential for diagnosing serious illnesses. As the AI gains more experience through training, the results will only become more accurate and give the doctors time to focus on the treatment rather than the diagnosis and mining though data or scanning images.

Medical imaging

In just one year, a leading medical facility in Texas generated more than half a million medical images in their fight against cancer. With there being so many images to analyse, harnessing the power of IoT was a must in early diagnosis to present the correct treatments. The facility installed a smart CT scanner that uses something known as computer vision. The scanner sends data directly to the cloud or a series of connected clouds and uses neural networks and deep learning algorithms to process that in a split second. The application is able to interpret the image from everything it has learnt in the past and identify the indicators of cancer that could have potentially gone unnoticed. This isn’t a slight against healthcare professionals but there are some early-stage symptoms that are virtually impossible to spot, and the AI was able to pinpoint those. Doctors are able to provide patients with an on-the-spot diagnosis and treatment plan. Smart image processing connected technologies like the CT Scanner will also allow medical device manufacturers to innovate. Integrating smart cloud platforms to medical devices they bring to market and licensing cloud analytics capabilities to their customers as a premium service. Subscription based cloud analytics services for medical diagnosis has the potential to drastically improve workflows by allowing for faster, more accurate diagnosis. There are obstacles around data privacy and ownership of decisions before image processing becomes mainstream, but it will be a part of the future with the clear advantages in accuracy of diagnosis and efficiency in repetitive and laborious tasks.

Managing beds

WiseWard use IBM Watson technology to predict when patients are likely to be discharged and their bed will be free. As providing quality care is heavily dependent on adequate bed space and the ability to move patients around, having technology like this is revolutionary for care and ensuring that patients get the experience they need. The AI platform can predict availability as much as five to seven days ahead using existing datasets including variables like gender, ward, surgery type and patient age.

Repetitive jobs

Administrative tasks are thought to cost $18 billion in the healthcare industry. New technology including voice to text transcriptions could help quickly order tests or prescribe medications without manually writing charts and notes. One of the most beneficial uses of AI is in interpreting records and papers using natural language processing (another application of AI). It can take doctors hours, weeks or even months to research a disease or treatment plan but with AI, this can be completed in an instant. Doctors may eventually have the capability of providing an instant test result rather than keeping their patients waiting.

Virtual doctors and nursing assistants

AI applications can now offer 24/7 advice and care, reducing waiting room times whilst giving patients what they need whenever they need it. It is thought that virtual assistants in healthcare could ultimately save around $20 billion annually. They can answer questions, monitor patients and provide quick answers as well as provide regular communications and proactive notifications. If they are able to do these jobs, it reduces the time spent by doctors on repetitive tasks and allows them to focus on care.

Medication and inventory management

Using AI, medication and stocks can be tracked across departments and even locations with ease. Using sensors, data can be collected on where different equipment is and how long it is due to be there for. Each time medication is taken from stock, logs can be updated and ensure sufficient amounts are always available. Whilst you’d expect medical facilities to be doing this already, most have to do so manually which is prone to error.

Remote surgery

More powerful cloud computing and the invention of 5G technology should enable the opportunity for remote treatments like surgery. 5G will reduce latency to a point where data is transferred almost in real-time. With that, it is much faster than 4G and has a greater bandwidth. All of this combined will create a landscape where remote operations are very plausible. Robots can even analyse data from pre-op medical records to guide the instrument of the surgeon. Coupled with the remote location option, experts in their field can provide assistance without being physically present.

The future of AI in healthcare

AI has a bright future in the healthcare industry and this article only touches the surface on the possibilities. As new technology such as the Connected Cloud, Edge Computing and 5G become commonplace, the capabilities of innovation will be pushed further to save time, lower costs and ultimately improve accuracy.