构建和测试好友聊天机器人,一个安全,可信和值得信赖的人工智能聊天机器人,不会误导,幻觉或污名化物质使用障碍:开发和可用性研究。

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES
JMIR Human Factors Pub Date : 2025-05-07 DOI:10.2196/69144
Adam Jerome Salyers, Sheana Bull, Joshva Silvasstar, Kevin Howell, Tara Wright, Farnoush Banaei-Kashani
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引用次数: 0

摘要

背景:部署自然语言处理和机器学习的人工智能(AI)聊天机器人在医疗保健领域变得越来越普遍,以促进患者教育和推广;然而,像ChatGPT这样的生成式聊天机器人面临着挑战,因为它们可能会误导和产生幻觉。卫生保健系统对使用这些工具进行患者教育、获得护理和自我管理越来越感兴趣,但需要保证人工智能系统的安全性和可靠性。目的:建立一个安全的药物使用问题短信系统,用于药物使用障碍(SUD)筛查。该系统将依赖第三方供应商的数据传输,因此需要对受保护的健康信息进行可靠和可信的加密。方法:我们描述了构建人工智能聊天机器人的过程和规范,用户可以访问该聊天机器人,从德克萨斯大学圣安东尼奥健康科学中心附属的临床提供商Be Well Texas获得SUD的信息和筛查。结果:AI聊天机器人系统使用自然语言处理和机器学习对与SUD相关的专家策划内容进行分类。它说明了我们如何在数据传输和静态数据的数据加密方面遵守HIPPA(健康保险可移植性和责任法案)的最佳实践,同时仍然提供最先进的系统,该系统使用动态的、用户驱动的对话来进行关于SUD的对话,筛选SUD并访问SUD治疗服务。结论:最近呼吁关注用户权利的用户友好设计,以尊重数字权利和数字物质使用产品的法规,这表明本研究是及时和适当的,同时仍在推进人工智能领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building and Beta-Testing Be Well Buddy Chatbot, a Secure, Credible and Trustworthy AI Chatbot That Will Not Misinform, Hallucinate or Stigmatize Substance Use Disorder: Development and Usability Study.

Background: Artificially intelligent (AI) chatbots that deploy natural language processing and machine learning are becoming more common in health care to facilitate patient education and outreach; however, generative chatbots such as ChatGPT face challenges, as they can misinform and hallucinate. Health care systems are increasingly interested in using these tools for patient education, access to care, and self-management, but need reassurances that AI systems can be secure and credible.

Objective: This study aimed to build a secure system that people can use to send SMS with questions about substance use, and which can be used to screen for substance use disorder (SUD). The system will rely on data transfer via third party vendors and will thus require reliable and trustworthy encryption of protected health information .

Methods: We describe the process and specifications for building an AI chatbot that users can access to gain information on and screen for SUD from Be Well Texas, a clinical provider affiliated with the University of Texas Health Sciences Center at San Antonio.

Results: The AI chatbot system uses natural language processing and machine learning to classify expert-curated content related to SUD. It illustrates how we can comply with best practices in HIPPA (Health Insurance Portability and Accountability Act) compliance in data encryption for data transfer and data at rest, while still offering a state-of-the-art system that uses dynamic, user-driven conversation to dialogue about SUD, screen for SUD and access SUD treatment services.

Conclusions: Recent calls for attention to user-friendly design concerning user rights that honor digital rights and regulations for digital substance use offerings suggest that this study is timely and appropriate while still advancing the field of AI.

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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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