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A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine

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arxiv 2405.08603 v3 pith:4WH6KDAK submitted 2024-05-14 cs.CL

classification cs.CL
keywords llmsmllmssurveymodelslanguagelargemedicalmedicine
verification ladder T0 review T1 audit T2 compute T3 formal
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Since the release of ChatGPT and GPT-4, large language models (LLMs) and multimodal large language models (MLLMs) have attracted widespread attention for their exceptional capabilities in understanding, reasoning, and generation, introducing transformative paradigms for integrating artificial intelligence into medicine. This survey provides a comprehensive overview of the development, principles, application scenarios, challenges, and future directions of LLMs and MLLMs in medicine. Specifically, it begins by examining the paradigm shift, tracing the transition from traditional models to LLMs and MLLMs, and highlighting the unique advantages of these LLMs and MLLMs in medical applications. Next, the survey reviews existing medical LLMs and MLLMs, providing detailed guidance on their construction and evaluation in a clear and systematic manner. Subsequently, to underscore the substantial value of LLMs and MLLMs in healthcare, the survey explores five promising applications in the field. Finally, the survey addresses the challenges confronting medical LLMs and MLLMs and proposes practical strategies and future directions for their integration into medicine. In summary, this survey offers a comprehensive analysis of the technical methodologies and practical clinical applications of medical LLMs and MLLMs, with the goal of bridging the gap between these advanced technologies and clinical practice, thereby fostering the evolution of the next generation of intelligent healthcare systems.

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Cited by 3 Pith papers

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

  1. Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

    cs.AI 2026-03 conditional novelty 6.0 of 10

    A fixed, sampling-free score — per-token log-probability variance times (1 + average |image-vs-text probability shift|) — detects medical-VQA hallucinations better than semantic-entropy baselines in 13 of 16 settings.

  2. RadEyeVideo: Enhancing general-domain Large Vision Language Model for chest X-ray analysis with video representations of eye gaze

    cs.CV 2025-07 reject novelty 5.0 of 10

    A video-based eye-gaze prompt improved report generation and diagnosis for one general-purpose vision-language model, LLaVA-OneVision, but hurt or barely helped two others, and the main comparison to medical models re...

  3. Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation

    cs.CV 2025-05 conditional novelty 4.0 of 10

    Prompting multimodal LLMs with ground-truth bounding boxes and gaze durations improves chest X-ray report metrics, but the effect is inconsistent and relies on privileged annotations.

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