Exploring tech advances in healthcare.
Creating technology that makes a meaningful difference in people’s lives is the ultimate goal for the majority of innovative minds in the industry. When considering this through the lens of healthcare, it becomes an increasingly important goal and despite digital health coming on leaps and bounds in recent decades, there is still room for improvement.
Remote monitoring and other telemedicine advancements are revolutionising the healthcare industry — supplementing the efforts of healthcare professionals across the globe who are under increasing pressure as life expectancy continues to rise.
Artificial intelligence (AI) is set to play an ever growing role in future, and its ability to analyse and interpret vast amounts of health data provides a fitting catalyst for a shift toward what we like to call ‘smart care’ at AI Nexus Healthcare.
How Important Will AI be in The Future of Healthcare
AI is one of the most powerful yet underutilised technologies that can benefit healthcare. Healthtech developers have been focusing on machine learning, in particular deep learning, as the go to tool for building their applications. The primary reason for this focus on machine learning has been the general availability of educational material, courses and off the shelf code libraries.
Machine learning is an important subfield of AI. Deep learning as a subfield of machine learning offers powerful tools for recognising and classifying patterns in data – for example images, sound, and time series data. However, recognition is not cognition! Deep learning algorithms and applications are blackboxes that are incapable of explaining the justification or explanation of how they arrive at their results.
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For critical application domains such as healthcare, explainability is a prerequisite in helping clinicians have confidence in the recommendations of an AI system. Furthermore, building deep learning models requires high quality labelled data which is difficult to obtain in healthcare due to practical and regulatory reasons. Patterns identified by deep learning applications still need to be interpreted in a given context –and knowledge and experience are required for interpretation and to produce actionable advice in healthcare applications. Medical doctors spend decades specialising and learning to combine the art and science of medicine. Medical expertise is essential for the interpretation of deep learning results and to produce actionable recommendations for each patient. But, by focusing on deep learning, developers have been trying to code doctors out. So it’s not surprising that AI as machine learning or deep learning has had limited success and adoption in healthcare to date.
To better leverage the benefits of AI in healthcare, we effectively need to code doctors into our applications. If we can achieve this, AI can have a truly transformative impact on healthcare. Developers, then, must look beyond simple pattern recognition and data collection.
In addition to machine learning or deep learning, AI has other powerful techniques and tools it can offer developers. These techniques include knowledge representation and reasoning – that is tools that can capture and emulate cognition – as well as other types of machine learning. This goes well beyond rule engines which represent basic examples of AI that is not machine learning. Cognitive AI is not an algorithm. It involves a variety of tools and techniques and many years of application development experience in different domains to acquire the necessary skills to build complex healthcare applications. The full potential of AI in healthcare can be realised by a hybrid approach that combines the best of machine learning and Cognitive AI. This hybrid approach enables developers to build end-to-end solutions, from pattern recognition from any type of data all the way to diagnosis and actionable recommendations.
‘Hybrid AI’ is a concept that essentially strives to combine recognition with cognition and underpins everything we do at AI Nexus Healthcare. The term centres on identifying trends in data such as heart rate, blood pressure and oxygen saturation and converting this into actionable advice. What we’re suggesting here is a technology that can look to replicate the same diagnostic approach as a qualified clinician. Now it would be an overly ambitious and frankly undesirable claim to say that AI will replace the need for medical professionals in the future. However, it’s a much more realistic claim to suggest that AI could help provide informed advice that will serve not only to assist professionals in their role, but also act as a ‘check engine’ light for the average consumer, who may be considering a trip to the doctor or just want to gain a greater insight into their own body. Using technology as a supplementary tool is the key to achieving ‘smart care’ and a digital overview of the body can inform the decisions of both users and clinicians. The elderly population is growing exponentially and with advances in technology resulting
in people leading longer lives, it is likely to carry on doing so. This is the fundamental reason why AI and other health technologies will play a vital role in the future of the industry.
Prevention Over Cure
I’m always taken aback and motivated by the fact that 80% of chronic diseases are preventable. Given this, spotting early warning signs will become so important in limiting the strain on healthcare services. Detecting health issues at earlier stages of the disease cycle can significantly increase the scope for recovery and treatment. AI can offer insight into an individual’s overall health and encourage them to take action as soon as anomalies in data occur, which in turn will alleviate avoidable pressure on the health system. And not only will it mean better health and wellbeing for individuals, it will also save on treatment and care costs. By studying an abundance of health data, patterns can be recognised (recognition) and then translated into functional medical advice (cognition), which can be used to inform medical professionals or encourage consumers to visit them if they are at perceived risk. A great example of this is falls prevention among the elderly. This technology allows for the identification of people’s fall risk through tracking data such as heart block, sleep quality, walking gait and other biometrics. This data is then processed, rationalised and presented as functional advice for carers to act upon, which in many cases could be life-saving advice. Beyond this, facial scans have the capability of predicting strokes, whilst the antecedent signs of diabetes can also be recognised through blood sugar analysis. The opportunities are endless and are a simple result of advanced hybrid AI — arguably the future of digital health.
Why is it so Important to Make This Technology Accessible?
It may come as a surprise to some that despite its complexity and years of development, the end game of AI and other digital health technology is more often than not to present a simple platform for users. For example, smart home speakers which are a common feature of 21st-century households are highly complex yet extremely simple to use. The same applies to telemedicine. With leading minds in the industry working tirelessly to create innovative solutions, the ultimate goal is to produce something that has genuine use within society, so localising an abundance of complex neural networks into an appealing
user interface is challenging, yet paramount. Smartphone integration is undoubtedly one of the most important aspects to consider and given the digital age we are currently living in, collecting data by simply scanning your face or fingertip through an app is a great way to reach the masses. Of course, there is the issue of pricing. Making this technology unaffordable is not only limiting its prospective interest but also its potential, so it’s about striking the right balance in line with the market — with an underlying appreciation for the bigger picture. Essentially, ‘mass care’ is self care as, by helping people on a wider scale, it broadens the impact of what’s achievable. Making it affordable then, deepens its effectiveness. As much as digital health is an enterprise, it is truly about making a genuine difference in people’s lives and using the wonderful capabilities of technology to support and improve an industry that we have all come to rely on in our lifetime. Significant steps have already been made and the work we are doing at AI Nexus Healthcare is a standout example of that. We are creating a platform that is offering something nobody has seen before, and that’s what it’s all about – being on the sharp end of innovation. The future of digital health is an exciting one, and as we learn more about the human body alongside our technological capabilities this meeting of minds could truly spell a new age of ‘smart care’.
