The U.S. Food and Drug Administration (FDA), Health Canada, and the United Kingdom's Medicines and Healthcare products Regulatory Agency (MHRA) have jointly identified 10 guiding principles that can inform the development of Good Machine Learning Practice (GMLP). These guiding principles will help promote safe, effective, and high-quality medical devices that use artificial intelligence and machine learning (AI/ML). Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. They use software algorithms to learn from real-world use and in some situations may use this information to improve the product's performance. But they also present unique considerations due to their complexity and the iterative and data-driven nature of their development.

Artificial intelligence (AI) solutions increasingly are driving M&A deal volume in the medical device industry, according to GlobalData. Based on the current AI medical device M&A transaction trends, GlobalData expects that diagnostic imaging, specialized sectors like oncology and neurology medical equipment, and in-vitro diagnostics will continue to lead M&A deals in 2022 after achieving 18,698, 18,095, and 13,775 deals in 2021 respectively. "AI is being adopted in the healthcare industry to drive treatment innovation in areas like chronic disease management and optimizing operational decision making, with platforms like Nvidia's Clara Holoscan MGX and Paige AI Inc's Paige Lymph Node software," said Selena Yu, a medical devices analyst at GlobalData. Nvidia, a leader in developing specialty AI applications, launched a new platform, Clara Holoscan MGX, designed for medical devices and computational sensing systems. The platform uses AI to support medical device development, with the capability of processing multiple data streams simultaneously and visualizing biology in real-time.

The U.S. Food and Drug Administration recently partnered with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to issue guiding principles to align efforts and standards for artificial intelligence and machine learning medical device development in health care. "The FDA believes that artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day," said Jim McKinney, public affairs specialist at the FDA, in an email to The Well News. McKinney said the 10 guiding principles grew out of collaborative discussions with Health Canada and MHRA, and learning from several sectors that applied AI and ML technologies for years and have developed good practices that can be readily applied to the medical device industry. Evidence from published information, expert and other public perspectives and review experience was used to develop the guiding principles that will be used by the agency to lay the foundation for the development of Good Machine Learning Practice, which will unify international efforts for medical device development. Over the past decade the FDA has reviewed and authorized a growing number of devices legally marketed with machine learning and expects this trend to continue.

Machine learning and artificial intelligence (AI) have long been heralded as the future of transformative technologies. From diagnostic and imaging technologies to therapeutic applications and robotics, the potential for machine learning and AI technologies reaches almost every corner of the medtech world. So, what does that mean for the development and application of next-gen medical devices? Dave Saunders is the chief technology officer of Galen Robotics, an emerging surgical robotics company that specializes in a new line of robotic technologies that provide a cooperatively controlled surgical platform. The company aims to provide robot-assisted technologies that can extend increased precision and unprecedented tool stabilization to microsurgery procedures.

This case study concerns the use of software agent advisors to improve efficiency and quality in issue tracking activities of development teams at the world's largest medical device manufacturer. Each software agent monitors, interacts with, and learns from its environment and user, recognizing when and how to provide different kinds of advice and support to facilitate issue tracking activities without directly modifying anything or otherwise violating domain constraints. The deployed software agent has not only enjoyed regular and growing use, but contributed to significant improvements. Issue rejection was significantly reduced and more focused, yielding significant quality and efficiency gains such as fewer reviews by quality assurance. This success reflects the benefits of the underlying AI technology.