by
Sean Ruck, Contributing Editor | December 02, 2019
From the November 2019 issue of HealthCare Business News magazine
The FDA is considering a total product life cycle-based regulatory framework for these SaMD AI-CL technologies that would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device is maintained. For the 510(k) classification, the framework says if there’s a similar device that exists already, the premarket notification is sufficient for the device being introduced. For the premarket authorization however, there are levels of risk categories from one (the lowest) to four that new devices will be classified under.
Primo says although AI SaMD exists across a wide spectrum, from locked to continuously learning data systems, a common set of considerations for data management, deep training and performance acceleration can be applied to the entire spectrum.
Last year, the Medical Imaging and Technology Alliance or MITA organized an AI summit meeting to create an overview and inventory of the artificial intelligence initiatives in the Medical Imaging community. Many organizations and AI experts, ranging from professional organizations to user organizations, regulators, manufacturers and standards development organizations attended.
Among the topics discussed was the fact that AI algorithms trained to work with images from a particular brand/model CT or MRI scanner may not always provide exactly the same results with a different scanner. Experiences show that even subtle differences in image quality and signal-to-noise ratios may cause this effect. At the meeting’s conclusion, the idea was discussed to assign several organizations to work on AI standards, but in close coordination to prevent overlapping work, similar to the model of how DICOM standard was created.
While there’s a lot of work to be done, there are already valuable initiatives underway according to Primo. HIMSS started an HL7 AI workgroup. The workgroup defined some clinical use cases starting with wound care and breast cancer. They plan to develop a library with HL7 tools for AI. MITA meanwhile, will develop several AI use cases defined by ACR as basis for regulatory guidelines to address automatic notifications by AI programs and diagnostics applications. These use cases will be shared with FDA for discussion and regulatory purposes. So while FDA, indeed, faces challenges with AI, there are many committed professional organizations, SDOs, user organizations and many others to assist FDA.
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