CARE-RAG - Clinical Assessment and Reasoning in RAG
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914164960657408 |
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| author | Potluri, Deepthi Mathew, Aby Mammen DeWitt, Jeffrey B Rasgon, Alexander L. Hao, Yide Hong, Junyuan Ding, Ying |
| author_facet | Potluri, Deepthi Mathew, Aby Mammen DeWitt, Jeffrey B Rasgon, Alexander L. Hao, Yide Hong, Junyuan Ding, Ying |
| contents | Access to the right evidence does not guarantee that large language models (LLMs) will reason with it correctly. This gap between retrieval and reasoning is especially concerning in clinical settings, where outputs must align with structured protocols. We study this gap using Written Exposure Therapy (WET) guidelines as a testbed. In evaluating model responses to curated clinician-vetted questions, we find that errors persist even when authoritative passages are provided. To address this, we propose an evaluation framework that measures accuracy, consistency, and fidelity of reasoning. Our results highlight both the potential and the risks: retrieval-augmented generation (RAG) can constrain outputs, but safe deployment requires assessing reasoning as rigorously as retrieval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CARE-RAG - Clinical Assessment and Reasoning in RAG Potluri, Deepthi Mathew, Aby Mammen DeWitt, Jeffrey B Rasgon, Alexander L. Hao, Yide Hong, Junyuan Ding, Ying Artificial Intelligence Computation and Language Access to the right evidence does not guarantee that large language models (LLMs) will reason with it correctly. This gap between retrieval and reasoning is especially concerning in clinical settings, where outputs must align with structured protocols. We study this gap using Written Exposure Therapy (WET) guidelines as a testbed. In evaluating model responses to curated clinician-vetted questions, we find that errors persist even when authoritative passages are provided. To address this, we propose an evaluation framework that measures accuracy, consistency, and fidelity of reasoning. Our results highlight both the potential and the risks: retrieval-augmented generation (RAG) can constrain outputs, but safe deployment requires assessing reasoning as rigorously as retrieval. |
| title | CARE-RAG - Clinical Assessment and Reasoning in RAG |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.15994 |