mlsanityData to 7 Oct 2026

MedGellan: LLM-Generated Medical Guidance to Support Physicians

arXiv 2507.04431 ↗First posted 6 Jul 2025cs.AI cs.CLcited by 1

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Abstract (arXiv)

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 F1 score.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 6 Jul 2025.

Builds on

21 of 29 references.

References · 21

  1. 2024The Llama 3 Herd of Modelscited by 14,192
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9 more
  1. 2023MedDM:LLM-executable clinical guidance tree for clinical decision-makingcited by 19
  2. 2021Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosiscited by 15
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  7. 2024Learning To Guide Human Decision Makers With Vision-Language Modelscited by 3
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Hybrid Decision Making via Conformal VLM-generated Guidancecited by 0