Using IoT & AI in healthcare image recognition
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 (AI)
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.
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.
Obstacles for AI image processing in healthcare
The top international medical company Lunit have a vision to “develop advanced software for medical data analysis that goes beyond the level of human vision.” One issue they face is consumer trust. Back in late 2017, the Google AI mistook a picture of a tortoise for a gun due to an error in how the application can be tricked. There are team actively working on these sorts of problems continuously, but it is a challenge to embed IoT into healthcare whilst these things do still happen. In a similar vain, whilst any problem with AI get such big media focus (Alexa ordering items on its own), adoption is slow as society adapts.
Lunit say that they have now managed over 97% accuracy in in nodule detection which is mightily impressive if we bear in mind that a pass mark for doctors in MRCGP exams is 72%. Most people are coming round to the idea that AI is better at diagnosing patients but not the debate is with opacity and malpractice.
If the AI application does get a diagnosis wrong, who is to blame? What is the route for a claim? Unless somehow we get to 100% accuracy this is always going to be a problem. Others are saying that because AI is a bit like a black box and we will never necessarily know how it came to a decision, patients will never truly be able to trust it no matter how accurate it might be.
Other applications of AI in healthcare
Whilst image processing has the capability of changing how we perceive the healthcare industry, it is important to be aware of other developments that are already being use or coming soon. These are a few of the key ones.
– Managing beds and records: AI is being used to manage patient records and place them in the best room/bed based on their condition
– Repetitive jobs: AI can automate previously manual jobs such as analysing x-rays and scans
– Treatment design: Create patient treatment plans based on data
– Virtual doctors: AI applications are being used to offer 24/7 virtual doctor services and reduce waiting room times
– Medication and inventory management: Track medication and inventory across multiple locations and stocks
Other developments such as health monitoring (devices like Garmin and Fitbit) and drug creation through medicine analysis are also in their early stages but look set to revolutionise the industry.
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.