The future of healthcare: a marriage of AI and edge
Nick Offin, head of sales, marketing & operations at dynabook Europe, exploring how technologies like artificial intelligence (AI) and edge computing will power the next phase of healthcare innovation.
Healthcare is on the cusp of a revolution – one that will be driven by technologies like artificial intelligence (AI) and edge computing. Implementation of healthcare AI is expected to grow at an annual growth rate of 41.4% from 2020 to reach $51.3 billion by 2027, while edge cloud computing is expected to grow by 34.1% between now and 2025. As both technologies continue to mature, they are increasingly being included together in healthcare leader’s decision-making. The two technologies go hand in hand – AI is now a key use case for edge computing and edge is a significant enabler for AI.
Transforming the rate of biomedical discovery with AI
During the COVID-19 pandemic, the healthcare industry has turned to AI to accelerate COVID-19 research to unlock as much knowledge around the virus as possible to develop suitable antiviral medicines. Causaly, a UK-based technology company, is doing exactly that. The company developed a biomedical research discovery tool that allows anyone to run deep searches and find answers to complex research questions that would previously have taken weeks or months to find with traditional keyword search. Instead, Causaly’s AI can read, understand, and interpret vast databases of biomedical knowledge in seconds, enabling researchers to rapidly map epidemiology data, biomarker genes, molecular targets and identify potential treatment options.
Traditionally, these types of AI applications have been powered by data centres and cloud computing. Of course, big data will always be processed via the cloud. However, in time, AI has made its way closer to the user – into software and into Internet of Medical Things (IoMT) endpoints and other medical devices. For example, wearable health monitors such as ECG monitors and blood pressure monitors can collect and analyse data locally, which a patient can share with their doctor for an instant health evaluation.
As a result, more healthcare businesses involved in AI have started to realise the benefits of edge computing. In fact, Deloitte now predicts that more than 750 million edge AI chips – designed to enable on-device machine learning – will be sold this year.
Entering a data-driven healthcare decade with edge
AI is undoubtedly a data-heavy and computing intensive technology. Concerns around bandwidth, latency, security and cost pose significant hurdles for the majority of healthcare businesses, particularly when a matter of seconds could determine the outcome for a patient. Edge benefits AI by helping overcome these technological challenges.
Artificial intelligence has, quite literally, got a big data problem. For instance, software collecting information about patients recovering after surgery will track key vitals and learn from repeatedly observing the same scenario. With edge computing, rather than sending this data to the cloud or a distant data centre, clinicians can collect, analyse and act upon critical data closer to its source. This process not only reduces the amount of bandwidth required, but also saves backhauling costs. What’s more, bringing processing to the point of capture gains immediate value from the data and gives clinicians instant access to insights.
Processing data at the edge also brings reduced latency, perhaps one of the more obvious reasons for edge computing’s adoption in a field where seconds can mean the difference between life and death. However, as technologies and services become more distributed across a healthcare business’ network with the rise of telemedicine, latency will naturally occur. Where real-time critical decision making and actions are required to save lives, latency must be kept to a minimum. By locating key processing tasks closer to end users, edge computing can deliver faster and more responsive AI-based services to enable rapid decision making.
Privacy remains an unsolved challenge in the AI industry, and one the healthcare industry is tackling in its own right. While doctor-patient confidentiality has historically been to keep healthcare records sealed away, the industry is realising that the best way to gain insights into patients is to release these records, cross-reference files and create enriched data which can be used to boost patient care. However, systems need to be put in place so patients can be confident their data is being shared securely. Edge computing offers a solution to this security conundrum. With edge-based AI, patient information is stored and processed locally on a device, rather than being sent to the cloud. Only less time-intensive data sets need to be pushed to the cloud, the rest remain local. If there is less transfer of sensitive patient data between devices and the cloud, this means better security for healthcare businesses and for patients.
Edge AI is the next wave of AI in healthcare and many medical technology companies are recognising this. For example, GE Healthcare recently announced a new edge computing technology designed specifically for the needs of healthcare providers. Ultimately, as healthcare enters a data-driven decade, there’s a real need for data storage and data computation to be located on the device. Not forgetting other factors – speed, privacy and security – which enable clinicians to make faster, more informed decisions safely.