一个微调的大型语言模型聊天机器人用于多场景放射学癌症护理:随机对照试验的交互优化,情绪支持和提供者倦怠减少
Zhi Liu1, Farrin Orouji2, Fan Yang3
1Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, 400021, China.
Journal of translational medicine
|February 14, 2026
概括
一个新的AI聊天机器人改善了癌症患者的放射交互,增强了沟通并减少了医疗保健提供者的倦怠. 这种人工智能工具还提高了CT和MRI图像质量,优化了工作流程.
科学领域:
- 医疗保健中的人工智能
- 医学成像和放射学医学成像和放射学
- 临床心理学和瘤学
背景情况:
- 癌症患者的抑郁和焦虑率更高.
- 放射学工作流程中的沟通效率低下会加剧患者的痛苦和提供者倦怠.
- 医疗资源有限和临床需求高,需要创新的解决方案.
研究的目的:
- 开发和验证一个基于DeepSeek R1的放射检查聊天机器人 (REC).
- 优化癌症患者和放射学医疗保健提供者 (RHPs) 之间的临床相互作用.
- 为癌症患者提供情感支持,并减少RHP燃烧.
主要方法:
- 从三个医院收集了36511分钟的音频数据,包括预约分拣,预检准备和放射诊所服务.
- 开发了一个REC,对特定领域的对话数据和特定场景的提示进行了微调.
- 进行了两项随机对照子试验,包括1,424名和638名患者,比较RHP+REC与单独使用RHP.
主要成果:
- 在预约分类和预检准备期间,RHP+REC组显示出优异的对话事实性和患者满意度.
- 在RHP+REC组中观察到RHP燃烧 (疲劳) 的显著减少.
- 通过REC干预,CT和MRI图像质量显著改善.
- 在复杂的放射学诊所服务期间,REC显示出同情心和情绪调节的局限性.
结论:
- 基于DeepSeek R1的REC有效地增强了放射学中的临床相互作用和患者支持.
- 聊天机器人在减少医疗保健提供者的倦怠方面发挥了重要作用.
- REC提供了一种可扩展的方法来优化放射学工作流程并改善患者护理.
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