MedPAO: A Protocol-Driven Agent for Structuring Medical Reports
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908577496563712 |
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| author | Vaidya, Shrish Shrinath Palani, Gowthamaan Ramesh, Sidharth Balasubramanian, Velmurugan Selvam, Minmini Srinivasaraja, Gokulraja Krishnamurthi, Ganapathy |
| author_facet | Vaidya, Shrish Shrinath Palani, Gowthamaan Ramesh, Sidharth Balasubramanian, Velmurugan Selvam, Minmini Srinivasaraja, Gokulraja Krishnamurthi, Ganapathy |
| contents | The deployment of Large Language Models (LLMs) for structuring clinical data is critically hindered by their tendency to hallucinate facts and their inability to follow domain-specific rules. To address this, we introduce MedPAO, a novel agentic framework that ensures accuracy and verifiable reasoning by grounding its operation in established clinical protocols such as the ABCDEF protocol for CXR analysis. MedPAO decomposes the report structuring task into a transparent process managed by a Plan-Act-Observe (PAO) loop and specialized tools. This protocol-driven method provides a verifiable alternative to opaque, monolithic models. The efficacy of our approach is demonstrated through rigorous evaluation: MedPAO achieves an F1-score of 0.96 on the critical sub-task of concept categorization. Notably, expert radiologists and clinicians rated the final structured outputs with an average score of 4.52 out of 5, indicating a level of reliability that surpasses baseline approaches relying solely on LLM-based foundation models. The code is available at: https://github.com/MiRL-IITM/medpao-agent |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04623 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedPAO: A Protocol-Driven Agent for Structuring Medical Reports Vaidya, Shrish Shrinath Palani, Gowthamaan Ramesh, Sidharth Balasubramanian, Velmurugan Selvam, Minmini Srinivasaraja, Gokulraja Krishnamurthi, Ganapathy Artificial Intelligence The deployment of Large Language Models (LLMs) for structuring clinical data is critically hindered by their tendency to hallucinate facts and their inability to follow domain-specific rules. To address this, we introduce MedPAO, a novel agentic framework that ensures accuracy and verifiable reasoning by grounding its operation in established clinical protocols such as the ABCDEF protocol for CXR analysis. MedPAO decomposes the report structuring task into a transparent process managed by a Plan-Act-Observe (PAO) loop and specialized tools. This protocol-driven method provides a verifiable alternative to opaque, monolithic models. The efficacy of our approach is demonstrated through rigorous evaluation: MedPAO achieves an F1-score of 0.96 on the critical sub-task of concept categorization. Notably, expert radiologists and clinicians rated the final structured outputs with an average score of 4.52 out of 5, indicating a level of reliability that surpasses baseline approaches relying solely on LLM-based foundation models. The code is available at: https://github.com/MiRL-IITM/medpao-agent |
| title | MedPAO: A Protocol-Driven Agent for Structuring Medical Reports |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.04623 |