开发和评估用于药物调查的轻量级大型语言聊天机器人
Kabilan Elangovan1,2, Jasmine Chiat Ling Ong3,4,5, Liyuan Jin1,2,6
1Singapore Health Services, Artificial Intelligence Office, Singapore.
PLOS digital health
|September 4, 2025
概括
我们开发了Med-Pal, 一个轻量级的医疗聊天机器人, 提供高质量的响应,解决数字健康中的计算和可访问性挑战.
科学领域:
- 人工智能
- 医疗信息学
- 自然语言处理
背景情况:
- 大型语言模型 (LLM) 显示了数字健康的潜力,但面临着计算和数据安全的挑战.
- 某些地区的互联网可访问性有限限制了大规模人工智能模型的部署.
- 对于医疗保健应用而言,需要高效的特定领域的LLM.
研究的目的:
- 开发和评估一个名为Med-Pal的轻量级,医疗领域特定的LLM聊天机器人.
- 解决数字健康应用中的计算限制和数据安全问题.
- 创建一个强大的临床LLM绩效评估框架.
主要方法:
- 在精心策划的药物调查数据集 (1,100个问答对) 上微调五个开源的轻量级LLM (≤70亿个参数).
- 在231个与药物相关的查询中验证LLM.
- 介绍SCORE,这是一个由多学科专家团队进行临床判断的新评估标准.
- 选择表现最好的LLM作为Med-Pal, 实施安全护以防止对抗性提示.
主要成果:
- 在一个单独的测试数据集中,Med- Pal获得了71. 9%的高质量反应.
- 与Biomistral和Meerkat相比,Med- Pal的性能更为出色.
- 开发的SCORE框架能够有效地对LLM反应进行临床评估.
结论:
- Med-Pal的轻量级架构和临床调整使其适用于各种医疗环境,包括那些数字基础设施有限的环境.
- 这项研究强调了开发有效的,专门用于医疗应用的LLM的可行性.
- 护和专家评估对于安全可靠的临床LLM部署至关重要.
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