mlsanityData to 14 Oct 2026

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

arXiv 2505.18695 ↗First posted 24 May 2025cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 65 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

19 of 65 references.

References · 19

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7 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026AI Act Evaluation Benchmark: An Open, Transparent, and Reproducible Evaluation Dataset for NLP and RAG Systemscited by 0
  2. 2026A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiogramscited by 0
  3. 2026Comparative Analysis of Machine Learning based Intrusion Detection in Realistic IoT Networkscited by 0