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Large Language Models for Medicine: A Survey

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arxiv 2405.13055 v1 pith:A5T6J3GO submitted 2024-05-20 cs.CL cs.AIcs.CY

Large Language Models for Medicine: A Survey

classification cs.CL cs.AIcs.CY
keywords medicalllmsapplicationschallengesdirectionsmodelsadvancedaiming
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across various medical scenarios. In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced research directions and benefit researchers for future medical applications. We emphasize the advantages of medical LLMs in applications, as well as the challenges encountered during their development. Finally, we suggest directions for technical integration to mitigate challenges and potential research directions for the future of medical LLMs, aiming to meet the demands of the medical field better.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    cs.AI 2025-11 unverdicted novelty 6.0

    IMACT-CXR presents an integrated multi-agent system using AutoGen, Bayesian Knowledge Tracing, gaze feedback, and vision-language models to provide interactive tutoring for chest X-ray interpretation with preliminary ...

  2. M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation

    cs.CV 2024-08 unverdicted novelty 5.0

    M4CXR is a multi-modal large language model that performs multiple tasks in chest X-ray analysis including report generation with claimed SOTA clinical accuracy using chain-of-thought prompting.

  3. UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA

    cs.CV 2026-06 unverdicted novelty 4.0

    UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.

  4. Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models

    cs.CL 2024-08 unverdicted novelty 4.0

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