Human-AI Collaborations to Improve Accuracy and Mitigate Bias in Acute Dyspnea Diagnosis

项目总结 急性呼吸困难(呼吸急促)是急诊科就诊和 每年住院治疗的人数。心力衰竭、肺炎和慢性阻塞性肺病是最常见的 常见病因，2017年美国有250万人住院治疗。确定确切的原因 急性呼吸困难是极其重要但具有挑战性的，因为出现症状、实验室检测和 成像结果可能很难解释，特别是在老年人和合并疾病或 得了重病。高达30%的患者可能会出现诊断错误和不适当的治疗，这是 与较差的患者结局相关。人工智能(AI)工具已被提出以增强 提供者在诊断过程中处于有利地位，能够支持对急性胰腺炎的诊断评估 呼吸困难。然而，不准确的人工智能工具也会恶化临床医生的表现。因此，简单地保持 临床医生的“环路”并不是针对表现不佳的模式的保证后盾。这项提议旨在 使有效的临床医生-人工智能协作，以提高对急性呼吸困难的诊断准确性。我们建议： 1)评估计算策略，以提高用于支持临床医生的人工智能工具的稳健性 急性呼吸困难的诊断，2)加强临床医生和人工智能工具之间合作的测试策略，3) 在临床环境中前瞻性地评估急性呼吸困难人工智能工具，同时评估收集 临床医生反馈以支持持续的模型改进。我们的多学科团队由以下领域的专家组成 临床医学、计算机视觉、机器学习和人机交互 应对这些重要挑战。成功完成这项提案将产生一个健壮的、可推广的 急性呼吸困难人工智能工具，以增强医生在诊断评估急性呼吸困难。更广泛地说， 提案将产生可概括的知识，以支持更安全的开发和AI工具的集成 医疗保健设置。

PROJECT SUMMARY Acute dyspnea (shortness of breath) is one of the most common reasons for emergency department visits and hospitalizations each year. Heart failure, pneumonia, and chronic obstructive pulmonary disease are the most common etiologies, representing 2.5 million hospitalizations in the US in 2017. Determining the precise cause of acute dyspnea is critically important but challenging, as presenting symptoms, laboratory testing, and imaging results may be difficult to interpret, particularly in the elderly and patients with comorbid disease or severe illness. Diagnostic errors and inappropriate treatment may occur in up to 30% of patients, which is associated with worse patient outcomes. Artificial Intelligence (AI) tools have been proposed to augment providers in the diagnostic process and are well-positioned to support the diagnostic evaluation of acute dyspnea. However, inaccurate AI tools can also worsen clinician performance. Therefore, simply keeping clinicians “in-the-loop” is not a guaranteed back-stop against a poorly performing model. This proposal seeks to enable effective Clinician-AI collaborations to improve diagnostic accuracy in acute dyspnea. We propose to: 1) evaluate computational strategies to improve the robustness of an AI tool used to support clinicians in the diagnosis of acute dyspnea, 2) test strategies to enhance collaborations between clinicians and AI tools, 3) prospectively evaluate an acute dyspnea AI tool in a clinical environment while evaluating strategies to collect clinician feedback to enable ongoing model improvement. Our multidisciplinary team consisting of experts in clinical medicine, computer vision, machine learning, and human-computer interaction are well positioned to tackle these important challenges. Successful completion of this proposal will result in a robust, generalizable acute dyspnea AI tool to augment physicians in the diagnostic evaluation of acute dyspnea. More broadly, the proposal will lead to generalizable knowledge to support safer development and integration of AI tools across healthcare settings.