The impact of ChatGPT and LLMs on medical imaging stakeholders: Perspectives and use cases

Jiancheng Yang , Hongwei Bran Li , Donglai Wei
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引用次数: 3

Abstract

This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which possess remarkable language understanding and generation capabilities, are augmenting the interpretive skills of radiologists, enhancing patient-physician communication, and streamlining clinical workflows. The paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders, including businesses, insurance entities, governments, research institutions, and hospitals (nicknamed BIGR-H). Through detailed analyses, illustrative use cases, and discussions on the broader implications and future directions, this perspective seeks to raise discussion in strategic planning and decision-making in the era of AI-enabled healthcare.

Abstract Image

ChatGPT和llm对医学成像利益相关者的影响:观点和用例
这项研究调查了大型语言模型(LLM)在医学成像中的变革潜力,如OpenAI ChatGPT。在公共数据的帮助下,这些模型具有非凡的语言理解和生成能力,增强了放射科医生的解释技能,增强了医患沟通,并简化了临床工作流程。本文介绍了一个分析框架,用于呈现LLM与更广泛的医疗成像利益相关者生态系统之间的复杂互动,包括企业、保险实体、政府、研究机构和医院(昵称为BIGR-H)。通过详细的分析、说明性的用例以及对更广泛的影响和未来方向的讨论,这一观点试图在人工智能医疗时代的战略规划和决策中引发讨论。
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