SCH: AI-DOCTOR COLLABORATIVE MEDICAL DIAGNOSIS

SCH：AI-医生协同医疗诊断

• 批准号: 10688087
• 负责人: Hien Van Nguyen
• 金额: $ 21.87万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-22 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10688087
• 关键词: Address; Algorithms; Artificial Intelligence; Cardiovascular system; Cessation of life; Classification; Clinical; Cognitive; Collaborations; Computer software; Computer-Assisted Diagnosis; Creativeness; Detection; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic radiologic examination; Error Sources; Evaluation; Expert Systems; Fatigue; Goals; Hospitals; Human; Institution; Instruction; Intention; Joints; Knowledge; Learning; Lung nodule; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Medical; Methodology; Methods; Mission; Modeling; Monitor; Outcome; Pathologic; Performance; Principal Investigator; Process; Psychological reinforcement; Public Health; Pulmonary Embolism; Radiology Specialty; Research Design; Retrospective Studies; Reverse engineering; Scanning; System; Systems Integration; Thinking; Thoracic Radiography; Time; User-Computer Interface; Visual; Work; Workload; artificial intelligence algorithm; cancer diagnosis; computer framework; design; diagnostic accuracy; distraction; gaze; imaging modality; improved; innovation; iterative design; multidisciplinary; multimodality; neglect; next generation; novel; radiologist; success; theories; usability; user-friendly; visual information

Recent retrospective studies show that radiology's diagnostic error rates did not decrease significantly over the years. For example, missed lung cancer rates remain at 20-60% on chest radiography dependent on study design. This error contributes to 40,000-80,000 deaths annually in U.S. hospitals. This project aims to develop a computational framework for Al to collaborate with human radiologists on medical diagnosis tasks. To achieve this goal, we divide the project into three Aims, where the first two focus on fundamental theories, and the last one evaluates the proposed approaches on targeted applications. Aim 1: Develop computational principles for optimal Al-radiologist interaction. This Aim will develop a computational framework for guiding the interaction between radiologists and Al to achieve the best possible diagnostic performance while minimizing the time burden. Our framework consists of the first method for reverse-engineering radiologists' intention from the joint gaze and visual information based on reinforcement learning. This Aim is the first to provide an integrated system with gaze sensing, deep networks, and human radiologists. The knowledge from this Aim will fundamentally transform how one would build collaborative medical diagnosis systems. Aim 2: Design a user-friendly and minimally-interfering interface for radiologist-Al interaction. This Aim addresses an essential question of designing a minimally interfering interface that allows human radiologists to interact with Al models efficiently. Our proposed system combines an innovative "multimodal thinking with audio and gaze" (MTAG) methodology with user-centered iterative design. The process will result in a novel radiologist-Al collaborative interface that maximizes time efficiency while minimizing the amount of distraction. The outcome of this Aim will shed light on design principles for systems involving radiologists. Aim 3: Evaluation Plan. This Aim evaluates the proposed approaches in Aim 1-2 on two clinically important applications: i) Lung nodule detection and ii) pulmonary embolism. Lung cancer is the second most common cancer, and pulmonary embolism is the third most common cause of cardiovascular death. Studying how radiologists collaborate with Al to reduce diagnostic errors will lead to significant clinical impacts. RELEVANCE (See instructions): Diagnostic errors contribute to 40,000-80,000 deaths annually in U.S. hospitals. This project combines novel artificial intelligence (Al) algorithms, gaze monitoring software, and design principles to help doctors minimize diagnostic errors due to cognitive and perceptual biases. The project's success will fundamentally change how we design Al medical systems to increase diagnostic accuracy, save lives, reduce missed cancer diagnoses, improve public health, and advance NCl's mission.

最近的回顾性研究表明，放射学的诊断错误率并没有显着下降 这些年。例如，胸部 X 线检查的肺癌漏诊率仍保持在 20-60%，具体取决于 研究设计。这一错误每年导致美国医院 40,000-80,000 人死亡。该项目旨在 为人工智能开发一个计算框架，以便与人类放射科医生合作进行医疗诊断 任务。为了实现这一目标，我们将该项目分为三个目标，其中前两个目标侧重于基本目标 理论，最后一个评估针对目标应用所提出的方法。 目标 1：开发最佳人工智能与放射科医生交互的计算原理。这个目标将发展 一个计算框架，用于指导放射科医生和人工智能之间的交互以实现最佳效果 可能的诊断性能，同时最大限度地减少时间负担。我们的框架包括第一个 基于联合凝视和视觉信息的放射科医生意图逆向工程方法 强化学习。该 Aim 是第一个提供具有凝视感应、深度感应的集成系统。 网络和人类放射科医生。这一目标的知识将从根本上改变一个人的方式 将建立协作医疗诊断系统。 目标 2：为放射科医生与人工智能交互设计一个用户友好且干扰最小的界面。这 Aim 解决了设计一个干扰最小的界面的基本问题，该界面允许人类 放射科医生与人工智能模型有效互动。我们提出的系统结合了创新的“多式联运” 用音频和凝视思考”（MTAG）方法论，以用户为中心的迭代设计。该过程将 产生了一种新颖的放射科医生-人工智能协作界面，可以最大限度地提高时间效率，同时最大限度地减少 分心量。该目标的结果将阐明涉及系统的设计原则 放射科医生。 目标 3：评估计划。该目标评估了目标 1-2 中提出的两个临床方法 重要应用：i) 肺结节检测和 ii) 肺栓塞。肺癌位居第二 最常见的癌症，肺栓塞是心血管死亡的第三大常见原因。 研究放射科医生如何与 AI 合作减少诊断错误将带来重大的临床意义 影响。 相关性（参见说明）： 美国医院每年因诊断错误导致 40,000-80,000 人死亡。该项目结合了 新颖的人工智能 (Al) 算法、注视监测软件和设计原理可帮助医生 最大限度地减少由于认知和知觉偏差而导致的诊断错误。该项目的成功将从根本上 改变我们设计人工智能医疗系统的方式，以提高诊断准确性、挽救生命、减少漏诊 癌症诊断、改善公众健康并推进 NCl 的使命。