Technology Category
Machine Learning: Calibration & Uncertainty QuantificationNatural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsHumans and AI: Explainable AI (XAI) for Human Understanding
AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification
🤖 AI Summary
Addressing the longstanding challenge of jointly optimizing accuracy, interpretability, and computational efficiency in medical device regulatory classification, this study introduces the first multi-model AI assessment framework tailored for regulatory compliance decisions. We systematically benchmark traditional machine learning (XGBoost), deep learning (BiLSTM), pretrained language models (RoBERTa), and fine-tuned large language models (Llama-3). To enhance transparency, we propose a hybrid interpretability method integrating rule-based backtracking with SHAP and LIME, and pioneer a quantitative interpretability evaluation protocol specifically designed for regulatory contexts. Evaluated on real-world regulatory text data, our framework achieves a state-of-the-art accuracy of 92.3%. Compared to the best-performing black-box model, it improves interpretability scores by 41% and reduces inference energy consumption by 67%, thereby significantly strengthening the trustworthiness and practical utility of FDA and CE classification decisions.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing AI models for medical device regulatory classification
Balancing accuracy, interpretability, and computational efficiency
Evaluating diverse AI approaches for regulatory decision-making
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses AI models for medical device classification
Evaluates accuracy, interpretability, and computational cost
Includes traditional ML, deep learning, and LLMs
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