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FDA's PCCP: Opportunities and Gaps
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DOI：10.1007/978-3-032-05663-4_6.png)
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Abstract
En 中文
The dynamic nature of medical imaging data poses a significant regulatory challenge for AI-based Software as a Medical Device (SaMD), as it requires constant adaptation. Traditionally, each of these modifications would require the SaMD to go through the complete approval process again, limiting the real-world deployment of AI-assisted SaMDs. The U.S. FDA's Predetermined Change Control Plan for Artificial Intelligence-Enabled Device Software Functions (PCCP) aims to bridge this gap. It allows, under certain conditions, a significantly simplified approval process for updated AI-enabled SaMDs. In this work, we discuss the great potential that this brings for the dynamic reality of medical imaging, but also explain three potential gaps in the current regulation. These concern an evaluation gap that poses a potential loophole from modified test data, an intended use gap that limits flexibility for unexpected and time-critical events such as a pandemic, and the foundation model gap that limits the applicability of this emerging technology. For each of those gaps, we present a solution to fully leverage the potential of PCCP as a regulatory framework enabling technologies that address the dynamic reality of medical imaging.
Keywords:
PCCP
Continual Learning
Medical Imaging
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