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Artificial General Intelligence for Medical Imaging Analysis

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arxiv 2306.05480 v4 pith:6G6QZLCD submitted 2023-06-08 cs.AI

classification cs.AI
keywords modelsmedicalimaginggenerallargellmsapplicationsartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.

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Cited by 2 Pith papers

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

  1. Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

    eess.IV 2025-06 conditional novelty 4.0 of 10

    A broad survey that organizes prompt mechanisms for medical image generation, segmentation, and classification into a two-dimensional taxonomy of core technologies and clinical applications.

  2. Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

    cs.CL 2025-02 conditional novelty 4.0 of 10

    Open LLMs (LLaMA-2, LLaMA-3, Mistral, Meditron) roughly match GPT-4 on a 25-patient prescription-suitability check when given SmPC context via RAG, though some interaction classes degrade with RAG.

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