对比多种AI模型在医疗器械分类中的表现，找平衡点。
AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification
- 测试传统机器学习、深度学习和大语言模型在分类任务中的表现。
- 发现大语言模型准确率最高，但计算成本显著增加。
- 适合关注模型可解释性与效率的医疗监管领域研究者。
监管事务作为医学与法律的交汇点，能从AI自动化中获益显著。分类任务是制造商向监管机构申报产品的第一步，对市场准入、监管审查及患者安全具有关键影响。本研究基于医疗器械描述的监管数据集，评估了包括传统机器学习（ML）、深度学习架构以及大语言模型在内的多种AI模型。从准确性、可解释性和计算成本三个维度进行综合评估，旨在为医疗设备分类提供更优的AI解决方案。
原文摘要 · Abstract (English)
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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