评估AI聊天机器人的性能,以回答患者关于腰部疼痛的常见问题
Simone P S Scaff1, Felipe J J Reis2, Giovanni E Ferreira3
1Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de Sao Paulo, Sao Paulo, Brazil.
Annals of the rheumatic diseases
|January 28, 2025
概括
大型语言模型 (LLM) 聊天机器人为腰部疼痛 (LBP) 患者提供适度准确的信息,但可读性和准确性因主题而异,影响患者的理解.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 腰部疼痛 (LBP) 是一种普遍的疾病,需要提供可访问的患者信息.
- 大型语言模型 (LLM) 越来越多地用于与健康有关的查询.
- 评估LLM产生的健康建议的可靠性至关重要.
研究的目的:
- 为了评估LLM聊天机器人对常见的腰部疼痛 (LBP) 患者问题的答案的准确性和可读性.
- 为了比较不同的LLM聊天机器人的性能 (ChatGPT 3.5,Bing,Bard/Gemini,ChatGPT 4.0).
主要方法:
- 对30个LBP相关问题的横截面分析.
- 来自四个LLM聊天机器人的答案根据LBP指南进行了准确性评估.
- 使用弗莱什阅读易度得分 (FRES) 测量可读性.
主要成果:
- 在1069个建议中,55.8%是准确的,42.1%是不准确的.
- 风险因素的不准确率最高;治疗和自我管理更准确.
- 在LLM的回答中,阅读"相当困难" (平均FRES 50.94).
结论:
- LLM-聊天机器人显示了LBP患者教育的潜力,但准确度可变.
- 不一致的可靠性和可读性可能会阻碍患者理解LBP信息.
- 需要进一步精细化,以确保可靠的LLM产生的健康建议.
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