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正在開發技術以評估醫療反應的可靠性和準確性
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科技公司獲得了一項資助,支持負責國家安全的高級研究項目局(ARPA-H)在其他事務協議下的聊天機器人準確性與可靠性評估(CARE)探索主題。CARE旨在開發高級工具和技術,以評估患者面對應用中的醫療聊天機器人,滿足在準確性可能影響患者結果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在價值,但顯著的侷限性威脅着它們的有效性。許多人工智能系統生成事實不準確或誤導性的響應,可能導致混淆並對患者造成潛在風險。隨着醫療保健的發展,需要一個可擴展的系統,以確保醫療聊天機器人在任何環境中的一致性能。這一需求因持續缺乏標準化而加劇,這持續削弱了信心。
"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.
「評估醫療聊天機器人不僅需要簡單檢查正確答案;還需要深入了解這些系統如何滿足不同用戶的複雜需求,」BBN首席研究員Damianos Karakos博士說。
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長,開發智能聊天機器人的監測、評估與診斷系統(MEDIC)。這一全面解決方案將作爲評估醫療聊天機器人的技術框架,其核心能力包括:
- 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.
- 整合護理人員、患者和醫療專業人員的見解,以優化聊天機器人互動,有效回應他們的擔憂和期望。
- 檢索相關醫療文本,從而根據基於證據的數據源驗證聊天機器人的響應。
- 先進的提示工程,以從不同的人口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缺失或不準確的信息,這些方法利用先進的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."
"我們的目標是開發一個適應性框架,嚴格評估聊天機器人在真實場景下的表現,重點關注偏見、公平性和生成誤導信息的風險等關鍵方面,"卡拉科斯表示。"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提供的飲食建議,並將任何模糊的回應上報給醫療專業人員進一步審查。該倡議旨在改善在多種醫療環境中集成人工智能的護理。"
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.
由BBN主導的團隊包括約翰·霍普金斯大學(馬克·德雷澤教授)、約翰·霍普金斯大學醫學院和霍華德大學醫院。此項工作的開展地點位於馬薩諸塞州劍橋市、華盛頓特區和馬里蘭州巴爾的摩。
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項目局(ARPA-H)資助。本文檔中包含的觀點和結論爲作者個人意見,不應被解讀爲代表美國政府的官方政策,無論是明示還是暗示。
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科技公司
RTX BBN科技公司成立於1948年,提供先進的科技研究與開發,專注於國家安全優先事項。從ARPANEt到第一封電子郵件,再到首個保護量子密碼的地鐵網絡,BBN始終將先進研究轉化爲爲客戶提供創新解決方案。BBN冒險並挑戰常規,以創建分析與機器智能、網絡與傳感器、智能軟件與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
擁有超過185,000名全球員工的RTX正在推動科技和科學的極限,重新定義我們如何連接和保護我們的世界。通過行業領先的企業——柯林斯航空、普拉特與惠特尼、雷神公司——我們正在推進航空領域,工程整合防禦系統以確保操作成功,並開發下一代科技解決方案及製造,以幫助全球客戶應對最關鍵的挑戰。該公司2023年的銷售額爲690億美元,總部位於弗吉尼亞州阿靈頓。
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SOURCE RTX
來源:RTX
譯文內容由第三人軟體翻譯。