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) 根据图像数据（例如 X 射线扫描）做出更明智的医疗诊断。人工智能的最新发展使得利用人工智能处理大量影像数据来实现医疗诊断自动化成为可能。然而，实际上，人工智能在诊所的应用进展缓慢，因为大多数人工智能都充当“黑匣子”——医生无法理解人工智能为何做出特定的诊断，也无法在发现错误时纠正人工智能。因此，缺乏信任阻碍了人工智能融入并增强医生的工作。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互动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，并向人工智能传授新的医学知识以提高其性能。该项目的成果将有助于打造新一代人工智能医疗诊断系统，该系统可以通过与医生经济高效地沟通结果，同时让医生轻松、充分地控制底层流程，从而与人类医生进行协作。为了实现这些目标，研究人员寻求通过添加一个用户界面层来扩展医生与人工智能之间的交互带宽，该用户界面层可引导医生查看、询问和理解人工智能正在做什么，并使他们能够 将任务委托给人工智能，同时能够告诉或教导人工智能如何执行这些任务。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，并利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人工智能的发现。该项目还将研究使医生能够表达控制人工智能行为的意图的方法（例如，在运行时指定规则以补充现有模型的局限性）。 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