AI is revolutionizing healthcare, but not everyone is experiencing the benefits. The digital divide between those with Internet access and those without it continues to grow, and it’s impacting the quality and affordability of rural medicine. Experts have recognized that health equity requires the adoption of emerging technologies that can bridge the digital divide.

The problem is that most AI implementations are currently cloud-based, which means the deployment of smart medical devices is limited to those regions with fast and reliable Internet connections. In turn, not everyone is getting access to innovative medical AI technologies that improve patient outcomes and reduce costs.

Read on to learn how edge computing can help expand the use of AI in rural areas where medical care has previously been a challenge.

Why Rural Regions Need Innovative MedTech Solutions

There is health inequality in many rural areas that have fewer medical resources and lower quality care options. It’s harder to attract healthcare providers, and those that are available constantly deal with workforce shortages and lack of medical supplies. This often makes quality medical care more expensive in rural communities.

The digital divide is also a key contributor to healthcare inequality. Modern approaches like telemedicine and remote patient monitoring are ways to provide higher-quality healthcare to more patients, but they’re difficult to deliver without consistent Internet connectivity. This means those patients in rural regions who would benefit the most from virtual care can’t take advantage of it due to poor Internet access.

AI and computer vision can also help overcome staffing challenges in rural areas by automating many tasks, but again, poor Internet connectivity is a barrier to deploying many AI-powered medical devices. Rural medicine needs more innovative MedTech solutions that can bring AI to remote regions without reliable Internet connectivity.

How Edge AI Can Enable Smart Medical Devices Everywhere

While lack of connectivity is a barrier to cloud-based AI devices, local inferencing doesn’t require Internet access. Instead, AI inferencing can be performed locally on the device with an embedded system or on an edge server within the local intranet. This eliminates the challenges involved with processing large amounts of data in the cloud, such as high bandwidth consumption, high latency, and connectivity issues.

Smart medical devices that integrate edge AI capabilities can be deployed anywhere, leading to high-quality care at a much lower cost. Medical AI devices can save healthcare providers time and resources by analyzing patient data and aiding in medical decisions without relying on the Internet. That means these edge AI applications can reduce the number of medical professionals needed to facilitate a variety of healthcare services to communities with restricted access to healthcare.

Edge AI and computer vision can even enable emerging use cases like generating real-time imaging insights to augment the diagnostics process because there’s much lower latency than cloud-based alternatives. Another use case is using edge devices to collect valuable data from underserved communities for genomics and other life sciences disciplines, which can help generate more equitable data sets and bridge the growing data divide.

In short, edge AI can reduce the costs for diagnostics, time spent on tedious tasks, and human error — all while improving accessibility and quality of care to people in remote and underserved communities. This would be an enormous step in the right direction for healthcare equity.

Delivering Edge AI Devices Faster, Easier, and Cheaper

Those medical technology developers that integrate edge AI into their devices will be at the forefront of revolutionizing rural medicine, yet many companies are still hesitant to do so. There are substantial barriers to developing AI-powered medical devices from scratch, including stringent certification requirements and challenges with selecting compatible hardware for AI applications.

MBX Systems is a hardware specialist helping medical technology developers deliver AI-powered devices without long and expensive development cycles. Our engineering approach focuses on combining modular hardware building blocks that can get medical technology developers most of the way to a complete solution at a much lower price point.

We’re constantly finding innovative solutions to fast-track AI device development in healthcare and many other industries. In fact, we’ve just announced new embedded edge AI platforms for MedTech developers that expand our existing suite of hardware building blocks. Later this year, we’re also introducing new industrial-grade edge AI computing solutions based on NVIDIA IGX Orin for medical technology developers.

Want to learn more about the adoption of edge AI in the healthcare industry? Check out our recent solution brief: Medical AI: The Shift from Cloud Inferencing to Edge Inferencing