SCH: AI-DOCTOR COLLABORATIVE MEDICAL DIAGNOSIS

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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.