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 正在開發評估醫療保健應對措施的可靠性和準確性的技術
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.
馬薩諸塞州劍橋,2024年12月10日 /PRNewswire/ — RTX BBN Technologies獲得了一項獎項,以支持衛生高級研究計劃局(ARPA-H)在其他交易協議下的聊天機器人精度和可靠性評估(CARE)探索主題。CARE旨在開發先進的工具和技術,用於評估面向患者的應用程序中的醫療聊天機器人,以滿足在準確性可能影響患者預後的情況下對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.
儘管醫療聊天機器人具有潛力,但重大限制威脅着其有效性。許多人工智能系統產生的反應不符合事實或誤導性,可能會導致混亂並給患者帶來潛在風險。隨着醫療保健的發展,需要一個可擴展的系統來確保醫療聊天機器人在任何環境中都能保持穩定的性能。持續缺乏標準化加劇了這種需求,這繼續削弱信心。
"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這項工作的首席研究員達米亞諾斯·卡拉科斯博士說:「評估醫療聊天機器人需要的不僅僅是檢查正確答案;還需要深入了解這些系統如何滿足不同用戶的複雜需求。」
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:
爲了解決這個問題,BBN將利用其在機器學習、基於語言的信息處理和大型語言模型方面的專業知識來開發智能聊天機器人(MEDIC)系統的監控、評估和診斷。這個全面的解決方案將作爲評估醫療聊天機器人的技術框架,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.
- 整合來自護理人員、患者和醫療專業人員的見解,以優化聊天機器人的互動,有效解決他們的擔憂和期望。
- 檢索相關的醫學文本,根據循證數據源驗證聊天機器人的反應。
- 先進的即時工程設計,可從各種人口統計角度創建真實的互動。
- 使用多種評估方法檢測聊天機器人輸出中的缺失或不準確信息,這些方法使用高級信息提取和機器學習技術。
"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."
卡拉科斯說:「我們的目標是開發一個適應性強的框架,嚴格評估聊天機器人在現實場景中的性能,重點關注偏見、公平性和產生誤導性信息的風險等關鍵方面。」「例如,在產前護理中,準媽媽獲得準確的飲食指導以支持胎兒健康至關重要。MEDIC 將評估醫療聊天機器人給出的飲食建議,並將任何模棱兩可的回覆上報給醫療保健專業人員,以供進一步審查。該計劃旨在改善各種醫療環境中的人工智能綜合護理。」
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領導的團隊包括約翰·霍普金斯大學(馬克·德雷茲教授)、約翰·霍普金斯大學醫學院和霍華德大學醫院。這項工作正在馬薩諸塞州劍橋、哥倫比亞特區華盛頓和馬里蘭州巴爾的摩進行。
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.
這項研究部分由衛生高級研究計劃局(ARPA-H)資助。本文件所載的觀點和結論是作者的觀點和結論,不應被解釋爲代表美國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.
關於 RTX BBN 科技
RTX BBN Technologies成立於1948年,以國家安全優先事項爲重點,提供先進的技術研發。從ARPaNet到第一封電子郵件,再到第一個受量子密碼保護的地鐵網絡,BBN不斷將先進的研究轉變爲爲其客戶提供創新的解決方案。BBN 冒着風險和挑戰慣例,創建分析和機器智能、網絡和傳感器、智能軟件和系統以及物理科學領域的解決方案。
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.
關於 RTX
RTX 在全球擁有 185,000 多名員工,突破了技術和科學的極限,重新定義了我們連接和保護世界的方式。通過業界領先的業務——柯林斯航空航天公司、普惠公司和雷神——我們正在推進航空發展,設計綜合防禦系統以取得運營成功,並開發下一代技術解決方案和製造以幫助全球客戶應對最關鍵的挑戰。該公司總部位於弗吉尼亞州阿靈頓,2023年銷售額爲690億美元。
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SOURCE RTX
來源 RTX