CAREER: Expanding the Interaction Bandwidth between Physicians and AI

职业：扩大医生与人工智能之间的互动带宽

• 批准号: 2047297
• 负责人: Xiang Chen
• 金额: $ 54.81万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047297&HistoricalAwards=false
• 关键词: CAREER; Expanding; Interaction; Bandwidth; between

This project will enable physicians to use artificial intelligence (AI) to make more informed medical diagnoses from image data (e.g., X-ray scans). The recent development of AI makes it possible to automate medical diagnosis by using AI to process large amounts of imaging data. However, in reality the adoption of AI in clinics has been slow because most AI functions as "black boxes" -- physicians cannot see why AI makes a given diagnosis or to correct AI when seeing a mistake. As such, there is a lack of trust that prevents AI from being integrated into and enhancing physicians' work. This project develops methods to make AI's diagnosis explainable to physicians while allowing physicians to interact with and control how AI works, such as telling AI to adjust its parameters based on a specific patient's case and teaching new medical knowledge to AI to improve its performance. The outcome of this project will contribute to a new generation of AI-enabled medical diagnostic systems that can collaborate with human physicians by cost-effectively communicating results with physicians while giving them easy and sufficient control over the underlying process.To achieve these goals, the investigator seeks to expand the interaction bandwidth between physicians and AI by adding a user interface layer that guides a physician to see, ask and understand what AI is doing and enable them to delegate tasks to AI while being able to tell or teach how AI performs those tasks. Specifically, the investigator will conduct studies to understand physicians' need for explanatory information in existing practices, and use this information to co-design with physicians interactive visualization that enables physicians to comprehend the AI's findings via question-and-answer. This project will also investigate methods that enable physicians to express their intents to control the AI behavior (e.g., specifying rules at run-time to complement an existing model's limitation). The project will also enable physicians to convey their domain knowledge to AI to control its long-term behavior for future diagnoses (for example by extracting AI-learnable medical concepts from physicians' labels and annotations on medical imaging data).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将使医生能够使用人工智能（AI）从图像数据中做出更明智的医疗诊断（例如，X射线扫描）。人工智能的最新发展使得通过使用人工智能处理大量成像数据来自动化医疗诊断成为可能。然而，在现实中，人工智能在诊所中的应用一直很缓慢，因为大多数人工智能都是“黑匣子”--医生无法看到人工智能为什么会做出给定的诊断，也无法在看到错误时纠正人工智能。因此，缺乏信任阻碍了人工智能融入和加强医生的工作。该项目开发了一些方法，使人工智能的诊断能够向医生解释，同时允许医生与人工智能进行交互并控制人工智能的工作方式，例如告诉人工智能根据特定患者的情况调整其参数，并向人工智能教授新的医学知识以提高其性能。该项目的成果将有助于开发新一代支持AI的医疗诊断系统，该系统可以与人类医生合作，通过经济高效地与医生沟通结果，同时让他们轻松充分地控制底层过程。为了实现这些目标，研究人员试图通过添加用户界面层来扩展医生与AI之间的交互带宽，该用户界面层可以引导医生查看，询问和理解AI正在做什么，并使他们能够将任务委托给AI，同时能够告诉或教授AI如何执行这些任务。具体来说，研究人员将进行研究，以了解医生在现有实践中对解释性信息的需求，并使用这些信息与医生共同设计交互式可视化，使医生能够通过问答来理解AI的发现。该项目还将研究使医生能够表达他们控制AI行为的意图的方法（例如，在运行时指定规则以补充现有模型的限制）。 该项目还将使医生能够将他们的领域知识传达给人工智能，以控制其未来诊断的长期行为（例如，通过从医生的标签和医学成像数据上的注释中提取人工智能可学习的医学概念）。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology

• DOI: 10.1145/3577011
• 发表时间: 2023-04-01
• 期刊: ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION
• 影响因子: 3.7
• 作者: Gu, Hongyan;Liang, Yuan;Chen, Xiang Anthony
• 通讯作者: Chen, Xiang Anthony

Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

• DOI: 10.48550/arxiv.2208.12437
• 发表时间: 2022-08
• 期刊: ArXiv
• 作者: H. Gu;Mohammad Haeri;Shuo Ni;Christopher Kazu Williams;N. Zarrin-Khameh;S. Magaki;Xiangdong Chen