The promise of artificial intelligence (AI) technology is finally enjoying commercial success in many industries, including automotive, manufacturing, retail, and logistics, in the form of machine learning (ML) based approaches to tough problems such as computer vision, robotics, and natural language translation and understanding. AI technology adoption is also happening in the medical device industry, and it’s not just driving exciting new features and high-end functionalities into the devices themselves. This technology is also enabling manufacturers to reap the rewards from new service models in which, instead of just selling and supporting devices, they can capture recurring service revenue streams, while at the same time strengthen customer relationships.

Overview of Machine Learning, Models, and Artificial Intelligence

ML is a subset of the larger field of AI in which models are trained via exposure to massive amounts of data. The model uses this data to make a prediction, and if the prediction is correct, the model is strengthened. Likewise, if the prediction is incorrect, the model is then slightly modified in an attempt to improve its accuracy. During the ML training process, these permutations and slight adjustments can occur millions and billions (and trillions) of times, enabling the models to become quite accurate in their predictions. While this training time and effort can be quite lengthy, with some models being trained for months on thousands of machines, the final result is typically a program that can run on a much smaller (although still computationally large) device for what is called inferencing. Inferencing is the term used for using a ML-trained model to produce a predicted result. 

As an example, ML and inference has been demonstrated to detect cancer by processing digital X-rays. The process for doing this is to develop an ML model designed to convert X-ray images into segmented images where cancer instances will be highlighted. In the training phase, images of cancers as identified by expert radiologists are used to train the network to understand what is not cancer, what is cancer, and what different types of cancers look like. The more ML models are trained, the better they get at maximizing correct diagnoses and minimizing incorrect diagnoses. What this means is that ML depends on both smart model design but just as importantly on a massive quantity (like hundreds of thousands to millions) of well-curated data examples, where the cancer has been previously identified. By learning from the data, a model develops its own algorithm for identifying suspicious-looking regions and flagging them. AI inference is then used to suggest a result/diagnosis. The result of this effort is a tool that helps medical professionals, in this example, make better and more informed decisions regarding treatment.  

The use of ML and AI inference in medical applications has the ability to transform modern healthcare and save many, many lives. For this reason, medical device manufacturers are rapidly exploring ways to incorporate these capabilities into their devices.

In the past, ML and AI inference capabilities were only available from expensive high-end super computers and large computational systems designed specifically to perform inferencing. Fortunately, with the advent of new semiconductor devices developed specifically to accelerate AI workloads, this technology has now advanced so much that many solutions have come down to price points and form-factors that make it viable for mainstream markets in which AI can be incorporated into a wide range of low-cost systems.

Not All Inference Acceleration Is Created Equal

At the heart of every ML system is the AI accelerator that provides the power to process the massive amounts of data being trained, analyzed, and acted on. However, selecting the right inference accelerator is not always an easy decision for a medical device manufacturer. Chip companies from all over the globe are vying for a piece of this high-growth market, and it’s hard to tell one solution from another, particularly since the industry lacks any real performance benchmarks for useful comparisons. This poses problems for manufacturers who think they are installing an accelerator that performs like a Lamborghini, but find out later it fails to deliver on that performance target, perhaps taking ten times longer to complete its work. This poses serious problems when a physician is relying on the device to deliver fast, reliable, and accurate detection of critical conditions such as cancer.

While there are many factors to consider when choosing an AI accelerator, below are a few key things to focus on.

My Medical Device is Better…and I Get More Revenue?

As mentioned above, taking high-performance AI inference capabilities to mainstream, cost-effective products is not the only advantage of using AI inference accelerator technology. This technology also enables a manufacturer to adopt an as-a-service business model with recurring revenue streams and subscription services. Because the models are always evolving and improving, there will be opportunities to provide improved solutions over time. In the tech industry, this has taken the form of software as a service offerings. This not only provides a recurring revenue stream for the solution providers, but it’s also another opportunity to continually engage with customers to strengthen that relationship.

There are so many opportunities for AI and in particular AI inference technology in medical devices. It is an exciting time as we are seeing the fruits of decades of research in AI moving to mainstream markets and bringing real value to humanity.