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arXiv · 2507.04431

MedGellan: LLM-Generated Medical Guidance to Support Physicians

Debodeep Banerjee·Burcu Sayin·Stefano Teso·Andrea Passerini
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Abstract

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.

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Debodeep Banerjee, Burcu Sayin, Stefano Teso, Andrea Passerini. 2025-07-06. MedGellan: LLM-Generated Medical Guidance to Support Physicians. https://arxiv.org/abs/2507.04431

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