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明显下降