AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification

• URL: http://arxiv.org/abs/2505.18695v1
• Date: Sat, 24 May 2025 13:41:20 GMT
• Title: AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification
• Abstract summary: We investigate a broad range of AI models using a regulatory dataset of medical device descriptions.<n>We evaluate each model along three key dimensions: accuracy, interpretability, and computational cost.
• Score: 3.439579933384111
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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