How AI-Powered Healthcare Technology Functions in HealthTech
Beyond incredible practical uses, AI is also cost-effective for healthcare technology- AI applications will save $150B annually by 2026.
Healthcare Technology or HealthTech is one of the fastest-growing segments of healthcare. Some of the most advanced technology available is facilitating this growth, including artificial intelligence (AI). In the COVID-19 pandemic, AI is helping the industry cope with sudden high patient volumes, connecting patients with reported data, diagnosing and detecting illnesses, and making general processes more efficient. One company even uses AI to detect disease outbreaks, and was among the first organizations to alert the public about the respiratory virus in Wuhan.
Beyond incredible practical uses, AI is also cost-effective for healthcare – one report suggests that AI applications will save $150 billion annually for the US healthcare economy by 2026. Additionally, AI forces medical professionals to think more critically about gaps in care, communication, and protocols. Particularly in the pandemic, when practices from hospitals are taking place at home, AI can automate the role of staff without compromising in-person care.
However, there can be friction when bringing AI into medical workflows, as clinics need an in-depth AI strategy to successfully integrate the technology.
Some AI systems require pre-organized data or trained algorithms, and many existing medical systems are digital, when AI is better-suited to analog circuits. Before committing to AI solutions, it’s important to understand how it works and syncs with other systems.
Here’s how AI-powered technology functions in HealthTech:
The structure of AI
At its core, AI performs human tasks by understanding language, recognizing shapes and sounds, learning, and problem solving. The tech combines large amounts of data with fast, iterative processing and intelligent algorithms (a list of instructions that define a task for a computer to follow).
Narrow & General Intelligence
Generally speaking, there are two types of AI. The first is Narrow Intelligence, which focuses on one task and can be used for things like filtering data or generating recommendations based on data. The second is General Intelligence, which is more sophisticated and can complete a wider scope of tasks – much like a human. Narrow Intelligence is the more commonly used form of AI right now, while General Intelligence is where the field is heading.
Machine learning (ML) is a subset of AI. Machine learning solutions are able to automatically learn and reiterate processes based on experience. ML solutions do not have to be explicitly programmed because they use algorithms. These algorithms allow software to automatically detect and learn from patterns or features in data. Pattern recognition can highlight patients at risk of developing a condition or seeing a condition deteriorate due to lifestyle, environmental, genomic or other factors.
Within machine learning, the subset of deep learning is inspired by the structure of the human brain and is made up of complex, layered neural networks. While machine learning needs structured data, deep learning relies on ANN (artificial neural networks) which put data in a hierarchy according to different concepts. In healthcare, deep learning is used in imaging to identify rare diseases or specific types of pathology.
AI works best when it’s unobtrusive and in the background. If developed correctly, it should transform data points into actionable measures for healthcare professionals and patients alike. That said, AI isn’t intended to replace doctors. Instead, the tech should help them perform to the top of their license and enhance proven medical approaches within the Healthcare Technology.
In medical imaging, graphical processing units (GPUs) provide the power for iterative processes used in AI. The specialized computer chips complete rapid mathematical equations to accelerate the projection of images in a frame. These circuits can be the ‘powerhouse’ for AI integrations, however, because each human genome is the equivalent of around 200GB, the energy needed to analyze medical imaging is substantial and expensive.
Elsewhere, there is huge potential for AI uses within the Internet of Things (IoT), which produces massive amounts of data from connected devices, much of which is untapped. In fact, by 2025, IoT devices are expected to generate 79.4 zettabytes (equal to one billion terabytes). Wearables, smart inhalers, and monitors can evaluate and condense numbers around a specific data point (e.g. glucose levels) and transfer it to other devices (e.g. an insulin pen) for personalized, automated care.
Because much of IoT software already has a functioning algorithm and is trained on data, the majority of vendors offer AI integrations. For example, a smart inhaler company has adopted AI to predict when patients are at risk of an asthma attack. Likewise, a company that acquires data around the quality of care for cancer patients has integrated AI solutions to predict appropriate treatments on a case-by-case basis.
Another way that AI can be integrated into HealthTech is via application programming interfaces (APIs). Such interfaces are portable packages of code that make it possible to include AI functionality in existing products and packages. APIs are essentially messenger code, which talk between software and algorithms, allowing the two technologies to run separately but still have a relationship. Perhaps the most popular API in the industry is the Cloud Healthcare API from Google, which allows HealthTech applications to build and run machine learning capabilities.
It’s also worth noting that existing algorithms can be replicated and repurposed. For instance, Microsoft partnered with Adaptive Biotechnologies in 2018, using algorithms that have been adopted from ones it currently uses for natural-language translation. The goal of the partnership is to create a universal blood test to detect diseases, infections, cancers, and autoimmune disorders in a person’s immune system.
‘A natural fit in healthcare’
In no way does AI undermine the capabilities of medical staff. Rather, it can more accurately compute and classify huge amounts of data to augment decisions made by professionals. Clinicians may synthesize a couple of streams of data at a given time but computer algorithms can synthesize millions of streams in the same period. Not to mention, AI algorithms look at vast troves of data and can connect the silos in ways they’ve never been synced before. It is undeniable then, that AI shapes an opportunity to help more people, at a faster pace, and offer viable alternative treatment.
AI is integral to software that aims to reason on input and explain on output. It’s logical nature is what makes it a natural fit in healthcare and all the more impactful on science as a whole.