MedGellan: LLM-Generated Medical Guidance to Support Physicians
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911143739523072 |
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| author | Banerjee, Debodeep Sayin, Burcu Teso, Stefano Passerini, Andrea |
| author_facet | Banerjee, Debodeep Sayin, Burcu Teso, Stefano Passerini, Andrea |
| contents | Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid frameworks that combine machine intelligence with human oversight offer a practical alternative. In this paper, we present MedGellan, a lightweight, annotation-free framework that uses a Large Language Model (LLM) to generate clinical guidance from raw medical records, which is then used by a physician to predict diagnoses. MedGellan uses a Bayesian-inspired prompting strategy that respects the temporal order of clinical data. Preliminary experiments show that the guidance generated by the LLM with MedGellan improves diagnostic performance, particularly in recall and $F_1$ score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04431 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedGellan: LLM-Generated Medical Guidance to Support Physicians Banerjee, Debodeep Sayin, Burcu Teso, Stefano Passerini, Andrea Artificial Intelligence Computation and Language Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid frameworks that combine machine intelligence with human oversight offer a practical alternative. In this paper, we present MedGellan, a lightweight, annotation-free framework that uses a Large Language Model (LLM) to generate clinical guidance from raw medical records, which is then used by a physician to predict diagnoses. MedGellan uses a Bayesian-inspired prompting strategy that respects the temporal order of clinical data. Preliminary experiments show that the guidance generated by the LLM with MedGellan improves diagnostic performance, particularly in recall and $F_1$ score. |
| title | MedGellan: LLM-Generated Medical Guidance to Support Physicians |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2507.04431 |