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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.00006 |
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| _version_ | 1866911412176027648 |
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| author | Kavishwar, Arun Lotter, William |
| author_facet | Kavishwar, Arun Lotter, William |
| contents | Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because the FDA's databases contain only limited metadata and non-searchable summary PDFs. To address this gap, we developed FDA AI Search, a website that enables semantic querying of FDA-authorized AI-enabled devices. The backend includes an embedding-based retrieval system, where LLM-extracted features from authorization summaries are compared to user queries to find relevant matches. We present quantitative and qualitative evaluation that support the effectiveness of the retrieval algorithm compared to keyword-based methods. As FDA-authorized AI devices become increasingly prevalent and their use cases expand, we envision that the tool will assist healthcare providers in identifying devices aligned with their clinical needs and support developers in formulating novel AI applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FDA AI Search: Making FDA-Authorized AI Devices Searchable Kavishwar, Arun Lotter, William Information Retrieval Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because the FDA's databases contain only limited metadata and non-searchable summary PDFs. To address this gap, we developed FDA AI Search, a website that enables semantic querying of FDA-authorized AI-enabled devices. The backend includes an embedding-based retrieval system, where LLM-extracted features from authorization summaries are compared to user queries to find relevant matches. We present quantitative and qualitative evaluation that support the effectiveness of the retrieval algorithm compared to keyword-based methods. As FDA-authorized AI devices become increasingly prevalent and their use cases expand, we envision that the tool will assist healthcare providers in identifying devices aligned with their clinical needs and support developers in formulating novel AI applications. |
| title | FDA AI Search: Making FDA-Authorized AI Devices Searchable |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.00006 |