Artificial intelligence and its variant machine learning continue to revolutionize the business world across the globe. Notably, AI is beginning to make inroads in the world of embedded devices, especially within the medical industry.
Of course, machine learning already helps physicians interpret medical imagery, improving their ability to make patient diagnoses. Here is a high-level overview of how AI and machine learning are transforming the design of medical devices. Use these insights to learn more about this revolutionary technology’s impact on this aspect of healthcare. The medical device sector offers copious opportunities for talented tech professionals and the companies employing them.
How Can AI and Machine Learning Transform Medical Device Design?
The Rapid Growth of AI-Powered Medical Devices
Over the last five years, the number of medical devices with embedded AI functionality has continued to grow. According to the FDA, the number of approved AI-enabled medical devices increased by 83 percent from 2018 to 2021. The FDA also recently created a Digital Health Center of Excellence to improve the organization’s medical device regulatory capabilities. The expansion in the number of AI-powered devices needing approval drove the creation of this center.
AI, Especially Making Inroads in Radiology and Cardiology
Notably, the highest number of AI-enabled medical devices submitted to the FDA boast use cases in radiology. They account for nearly 75 percent of all device submissions to the agency. Cardiology devices leveraging AI and machine learning make up 11 percent of the submissions.
In both scenarios, the massive data generated by images and electrocardiograms benefit from predictive analytics powered by machine learning. Doctors are able to analyze imagery and EKGs faster and more effectively with the help of AI. In fact, this capability appears to be the leading factor driving the growth of AI in medical device design.
As AI/ML Increases in Complexity, Regulatory Processes Become More Difficult
We established that AI in medical devices today largely focuses on predictive analytics use-cases in radiology and cardiology. However, as this technology continues to evolve and become more complex, expect regulatory processes to also grow in complexity. It especially becomes critical with AI-powered devices that self-learn or adapt over time. Most of the AI algorithms in currently approved medical devices remain static after deployment.
The FDA is developing a change control process for these medical devices after they reach the market. Regulators, software engineers, and clinicians need to work together to ensure patient safety for medical devices using self-learning AI.
Looking For Technology Professionals?
If you need technology professionals experienced in AI, contact the experts at The CERES Group. As one of the top technical staffing agencies in Boston, we provide top-shelf candidates able to meet your needs. Schedule some time with us to discuss your current hiring needs.
