Three strategies for unlocking the power of continuous integration/continuous deployment (CI/CD) for AI-powered medical devices
This article originally appeared in Medical Design and Outsourcing on November 12, 2024.
It’s no secret that AI-powered medical devices are revolutionizing healthcare, setting new standards for speed, precision, and accuracy in the diagnostics that drive better patient outcomes.
Artificial intelligence (AI) and machine learning (ML) technology is also at the forefront of the shift toward remote care, making it possible to deliver personalized treatments customized to each patient’s unique data, all while extending healthcare beyond traditional clinical settings.
As of August 2024, the FDA has greenlit a staggering 950 AI– and ML-enabled medical devices. With the surge of connected devices and continued investment in AI, the growth of authorized AI-enabled tools shows no signs of slowing down.
But as these complex systems multiply, so do the challenges of ensuring their safety and reliability. Medtech requires a proactive approach, one that tech giants such as Google and Amazon have made central to their continued success. A continuous integration/continuous deployment (CI/CD) strategy that champions frequent updates and adaptations isn’t optional for medtech developers — it’s essential. This piece will outline the best practices for designing CI/CD architectures and offer strategies to navigate the unique challenges of regulated environments.
