M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
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arXiv
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| Format: | Preprint |
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2025
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| author | Acharya, Arkadeep Ghosh, Akash Verma, Pradeepika Pasupa, Kitsuchart Saha, Sriparna Singh, Priti |
| author_facet | Acharya, Arkadeep Ghosh, Akash Verma, Pradeepika Pasupa, Kitsuchart Saha, Sriparna Singh, Priti |
| contents | With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve, spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications. The dataset and the baselines code are available in this github page https://github.com/AkashGhosh/M3Retrieve. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_06888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M3Retrieve: Benchmarking Multimodal Retrieval for Medicine Acharya, Arkadeep Ghosh, Akash Verma, Pradeepika Pasupa, Kitsuchart Saha, Sriparna Singh, Priti Information Retrieval Artificial Intelligence With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve, spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications. The dataset and the baselines code are available in this github page https://github.com/AkashGhosh/M3Retrieve. |
| title | M3Retrieve: Benchmarking Multimodal Retrieval for Medicine |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2510.06888 |