SCH: AI-DOCTOR COLLABORATIVE MEDICAL DIAGNOSIS

最近的回顾研究表明，放射学的诊断错误率并没有显著下降。 这些年。例如，胸部X光检查漏检率保持在20%-60%，这取决于 研究设计。这一错误每年导致美国医院40,000-80,000人死亡。这个项目的目的是 为人工智能开发一个计算框架，以便在医疗诊断方面与人类放射科医生合作 任务。为了实现这一目标，我们将项目划分为三个目标，其中前两个目标侧重于基础 最后一章针对目标应用对所提出的方法进行了评估。 目标1：制定最佳铝放射科医生互动的计算原则。这一目标将得到发展。 指导放射科医生和人工智能之间的互动以达到最佳效果的计算框架 尽可能提高诊断性能，同时最大限度地减少时间负担。我们的框架包括第一个 基于联合视线和视觉信息的放射科医生意图逆向工程方法 强化学习。这个目标是第一个提供一个集成的系统与凝视感知，深度 网络和人类放射科医生。来自这个目标的知识将从根本上改变一个人 将建立协作式医疗诊断系统。 目的2：设计一个界面友好、干扰最小的放射科医生-人工智能交互界面。这 Aim解决了一个基本问题，即设计一个干扰最小的界面，使人类 放射科医生与Al模型进行有效的交互。我们提议的系统结合了创新的“多式联运” 以用户为中心的迭代设计(MTAG)方法。该过程将 产生了一种新的放射科医生-Al协作界面，该界面最大限度地提高了时间效率，同时将 分心的程度。这一目标的结果将阐明系统的设计原则，包括 放射科医生。 目标3：评估计划。这一目标评估了目标1-2中提出的方法在两个临床上 重要应用：i)肺结节检测和ii)肺栓塞。肺癌是第二位的 最常见的癌症和肺栓塞是导致心血管疾病死亡的第三大常见原因。 研究放射科医生如何与Al合作以减少诊断错误将导致重大的临床 影响。 相关性(请参阅说明)： 诊断错误每年导致美国医院4万至8万人死亡。这个项目结合了 新的人工智能(Al)算法、凝视监控软件和设计原则，帮助医生 最大限度地减少由于认知和知觉偏差造成的诊断错误。该项目的成功将从根本上 改变我们设计医疗系统的方式，以提高诊断准确率、拯救生命、减少漏诊 癌症诊断，改善公共健康，推进NCL的使命。

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.