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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.18695 |
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| _version_ | 1866909622121529344 |
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| author | Han, Yu Ceross, Aaron Bergmann, Jeroen H. M. |
| author_facet | Han, Yu Ceross, Aaron Bergmann, Jeroen H. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification Han, Yu Ceross, Aaron Bergmann, Jeroen H. M. Artificial Intelligence 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. |
| title | AI for Regulatory Affairs: Balancing Accuracy, Interpretability, and Computational Cost in Medical Device Classification |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.18695 |