Salyers Adam Jerome, Bull Sheana, Silvasstar Joshva, Howell Kevin, Wright Tara, Banaei-Kashani Farnoush
Clinic Chat, LLC, 2950 Arkins Ct, Unit 605, Denver, CO, 80216, United States, 1 3038079800.
University of Texas Health Sciences at San Antonio, San Antonio, TX, United States.
JMIR Hum Factors. 2025 May 7;12:e69144. doi: 10.2196/69144.
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.
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 .
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.
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.
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.
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越来越普遍,以促进患者教育和宣传;然而,诸如ChatGPT之类的生成式聊天机器人面临挑战,因为它们可能会提供错误信息和产生幻觉。医疗保健系统越来越有兴趣将这些工具用于患者教育、医疗服务获取和自我管理,但需要确保人工智能系统是安全可靠的。
本研究旨在构建一个安全的系统,人们可以使用该系统发送有关物质使用问题的短信,并可用于筛查物质使用障碍(SUD)。该系统将依赖第三方供应商进行数据传输,因此需要对受保护的健康信息进行可靠且值得信赖的加密。
我们描述了构建一个人工智能聊天机器人的过程和规范,用户可以通过该聊天机器人从与圣安东尼奥德克萨斯大学健康科学中心相关联的临床服务提供商“健康德州”获取有关SUD的信息并进行SUD筛查。
人工智能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加密方面遵守《健康保险流通与责任法案》(HIPPA)合规的最佳实践,同时仍提供一个使用动态、用户驱动对话来讨论SUD、筛查SUD并获取SUD治疗服务的先进系统。
最近呼吁关注尊重数字权利和数字物质使用产品法规的用户权利方面的用户友好设计,这表明本研究既及时又恰当,同时仍在推动人工智能领域的发展。