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.

本研究探讨了大型语言模型，如 OpenAI ChatGPT 在医学成像方面的变革潜力。通过公共数据辅助，这些模型增强了放射科医生的解释技能，提高患者与医生之间的沟通，并简化了临床工作流程。本文提出了一个分析框架，解释了 LLMs 与医学成像利益相关者（包括企业、保险机构、政府、研究机构和医院）之间的复杂相互作用，通过详细分析、实例应用和探讨其广泛影响及未来方向，旨在引发就 AI 助力医疗保健的战略规划和决策的讨论。

ChatGPT 和 LLMs 对医学影像相关人员的影响：视角和使用案例

The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders:  Perspectives and Use Cases

Language barriers present a great challenge in our increasingly connected and
global world. Especially within the medical domain, e.g. hospital or emergency
room, communication difficulties and delays may lead to malpractice and
non-optimal patient care. In the HYKIST project, we consider patient-physician
communication, more specifically between a German-speaking physician and an
Arabic- or Vietnamese-speaking patient. Currently, a doctor can call the
Triaphon service to get assistance from an interpreter in order to help
facilitate communication. The HYKIST goal is to support the usually
non-professional bilingual interpreter with an automatic speech translation
system to improve patient care and help overcome language barriers. In this
work, we present our ASR system development efforts for this conversational
telephone speech translation task in the medical domain for two languages
pairs, data collection, various acoustic model architectures and
dialect-induced difficulties.

在医疗领域中，德语医生与阿拉伯或越南语患者间的患者 - 医生交流，由于语言屏障会导致沟通困难和延误，为了支持非专业双语翻译，该研究开发了一个自动语音翻译系统，该系统能够帮助改善患者护理并克服语言障碍。研究者针对两种语言对中的医疗领域的电话会话语音翻译任务进行了 ASR 系统开发。