AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification
Journal:
arXiv
Published Date:
May 24, 2025
Abstract
Regulatory affairs, which sits at the intersection of medicine and law, can
benefit significantly from AI-enabled automation. Classification task is the
initial step in which manufacturers position their products to regulatory
authorities, and it plays a critical role in determining market access,
regulatory scrutiny, and ultimately, patient safety. In this study, we
investigate a broad range of AI models -- including traditional machine
learning (ML) algorithms, deep learning architectures, and large language
models -- using a regulatory dataset of medical device descriptions. We
evaluate each model along three key dimensions: accuracy, interpretability, and
computational cost.
