RTX BBN Technologies to Support ARPA-H AI-powered Medical Chatbots Reliability Evaluation Effort
RTX BBN Technologies to Support ARPA-H AI-powered Medical Chatbots Reliability Evaluation Effort
BBN developing technology to assess the reliability and accuracy of healthcare responses
BBN 正在开发评估医疗保健应对措施的可靠性和准确性的技术
CAMBRIDGE, Mass., Dec. 10, 2024 /PRNewswire/ -- RTX BBN Technologies received an award to support the Advanced Research Projects Agency for Health's (ARPA-H) Chatbot Accuracy and Reliability Evaluation (CARE) Exploration Topic under an Other Transaction Agreement. CARE aims to develop advanced tools and technologies for evaluating medical chatbots in patient-facing applications, addressing the critical need for reliable health information in situations where accuracy may influence patient outcomes.
马萨诸塞州剑桥,2024年12月10日 /PRNewswire/ — RTX BBN Technologies获得了一项奖项,以支持卫生高级研究计划局(ARPA-H)在其他交易协议下的聊天机器人精度和可靠性评估(CARE)探索主题。CARE旨在开发先进的工具和技术,用于评估面向患者的应用程序中的医疗聊天机器人,以满足在准确性可能影响患者预后的情况下对可靠健康信息的迫切需求。
Despite the potential of medical chatbots, significant limitations threaten their effectiveness. Many AI systems generate factually inaccurate or misleading responses that may cause confusion and pose potential risk to patients. As healthcare evolves, a scalable system is needed to ensure consistent medical chatbot performance in any setting. This need is intensified by ongoing lack of standardization, which continues to undermine confidence.
尽管医疗聊天机器人具有潜力,但重大限制威胁着其有效性。许多人工智能系统产生的反应不符合事实或误导性,可能会导致混乱并给患者带来潜在风险。随着医疗保健的发展,需要一个可扩展的系统来确保医疗聊天机器人在任何环境中都能保持稳定的性能。持续缺乏标准化加剧了这种需求,这继续削弱信心。
"Evaluating medical chatbots requires more than simply checking for correct answers; it demands a deep understanding of how these systems address the complex needs of diverse users," said Dr. Damianos Karakos, BBN principal investigator on the effort.
BBN这项工作的首席研究员达米亚诺斯·卡拉科斯博士说:“评估医疗聊天机器人需要的不仅仅是检查正确答案;还需要深入了解这些系统如何满足不同用户的复杂需求。”
To address this problem, BBN will use its expertise in machine learning, language-based information processing and large language models to develop the Monitoring, Evaluation and Diagnosing of Intelligent Chatbots (MEDIC) system. This comprehensive solution will function as a technological framework for evaluating medical chatbots, featuring core capabilities such as:
为了解决这个问题,BBN将利用其在机器学习、基于语言的信息处理和大型语言模型方面的专业知识来开发智能聊天机器人(MEDIC)系统的监控、评估和诊断。这个全面的解决方案将作为评估医疗聊天机器人的技术框架,其核心功能包括:
- Integration of insights from caregivers, patients and medical professionals to optimize chatbot interactions and effectively address their concerns and expectations.
- Retrieval of relevant medical texts to validate chatbot responses against evidence-based data sources.
- Advanced prompt engineering to create realistic interactions from various demographic perspectives.
- Detection of missing or inaccurate information in chatbot outputs using multiple evaluative methods, which use advanced information extraction and machine learning techniques.
- 整合来自护理人员、患者和医疗专业人员的见解,以优化聊天机器人的互动,有效解决他们的担忧和期望。
- 检索相关的医学文本,根据循证数据源验证聊天机器人的反应。
- 先进的即时工程设计,可从各种人口统计角度创建真实的互动。
- 使用多种评估方法检测聊天机器人输出中的缺失或不准确信息,这些方法使用高级信息提取和机器学习技术。
"Our goal is to develop an adaptable framework that rigorously assesses chatbot performance in real-world scenarios, focusing on key aspects like bias, fairness and the risk of generating misleading information," said Karakos. "For example, in prenatal care, it's crucial that expectant mothers receive accurate dietary guidance to support fetal health. MEDIC will assess the dietary advice given by medical chatbots and escalate any ambiguous responses to healthcare professionals for further review. This initiative aims to improve AI-integrated care in a variety of healthcare settings."
卡拉科斯说:“我们的目标是开发一个适应性强的框架,严格评估聊天机器人在现实场景中的性能,重点关注偏见、公平性和产生误导性信息的风险等关键方面。”“例如,在产前护理中,准妈妈获得准确的饮食指导以支持胎儿健康至关重要。MEDIC 将评估医疗聊天机器人给出的饮食建议,并将任何模棱两可的回复上报给医疗保健专业人员,以供进一步审查。该计划旨在改善各种医疗环境中的人工智能综合护理。”
The BBN-led team includes Johns Hopkins University (Prof. Mark Dredze), Johns Hopkins University School of Medicine and Howard University Hospital. Work on this effort is being performed in Cambridge, Massachusetts; Washington, D.C.; and Baltimore, Maryland.
BBN领导的团队包括约翰·霍普金斯大学(马克·德雷兹教授)、约翰·霍普金斯大学医学院和霍华德大学医院。这项工作正在马萨诸塞州剑桥、哥伦比亚特区华盛顿和马里兰州巴尔的摩进行。
This research was, in part, funded by the Advanced Research Projects Agency for Health (ARPA-H). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Government.
这项研究部分由卫生高级研究计划局(ARPA-H)资助。本文件所载的观点和结论是作者的观点和结论,不应被解释为代表美国政府明示或暗示的官方政策。
About RTX BBN Technologies
Founded in 1948, RTX BBN Technologies provides advanced technology research and development with a focus on national security priorities. From the ARPANET to the first email, through the first metro network protected by quantum cryptography, BBN consistently transitions advanced research to produce innovative solutions for its customers. BBN takes risks and challenges conventions to create solutions in analytics and machine intelligence, networks and sensors, intelligent software and systems, and physical sciences.
关于 RTX BBN 科技
RTX BBN Technologies成立于1948年,以国家安全优先事项为重点,提供先进的技术研发。从ARPaNet到第一封电子邮件,再到第一个受量子密码保护的地铁网络,BBN不断将先进的研究转变为为其客户提供创新的解决方案。BBN 冒着风险和挑战惯例,创建分析和机器智能、网络和传感器、智能软件和系统以及物理科学领域的解决方案。
About RTX
With more than 185,000 global employees, RTX pushes the limits of technology and science to redefine how we connect and protect our world. Through industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon – we are advancing aviation, engineering integrated defense systems for operational success, and developing next-generation technology solutions and manufacturing to help global customers address their most critical challenges. The company, with 2023 sales of $69 billion, is headquartered in Arlington, Virginia.
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RTX 在全球拥有 185,000 多名员工,突破了技术和科学的极限,重新定义了我们连接和保护世界的方式。通过业界领先的业务——柯林斯航空航天公司、普惠公司和雷神——我们正在推进航空发展,设计综合防御系统以取得运营成功,并开发下一代技术解决方案和制造以帮助全球客户应对最关键的挑战。该公司总部位于弗吉尼亚州阿灵顿,2023年销售额为690亿美元。
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