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

人机协作提高急性呼吸困难诊断的准确性并减少偏差

• 批准号: 10272748
• 负责人: Michael William Sjoding
• 金额: $ 70.53万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10272748
• 关键词: Acute; Algorithms; Artificial Intelligence; Back; Caring; Chronic Obstructive Airway Disease; Clinical; Clinical Medicine; Clinical/Radiologic; Collaborations; Complex; Computer Vision Systems; Congestive Heart Failure; Controlled Environment; Data; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic Errors; Disadvantaged; Disease; Dyspnea; Electronic Health Record; Emergency department visit; Ensure; Environment; Etiology; Evaluation; Feedback; Female; Healthcare; Heart failure; Hospitalization; Human; Hybrids; Image; Institution; Intelligence; Knowledge; Laboratories; Lead; Learning; Machine Learning; Minority Groups; Modeling; Patient Care; Patient-Focused Outcomes; Patients; Performance; Physicians; Pneumonia; Positioning Attribute; Prevalence; Process; Provider; Retinal blind spot; Shortness of Breath; Symptoms; Techniques; Testing; Training; Trust; Uncertainty; Vision; Work; accurate diagnosis; base; clinical decision support; clinically relevant; comorbidity; computer human interaction; diagnostic accuracy; health care settings; improved; male; multidisciplinary; multitask; older patient; patient population; patient subsets; prospective; prospective test; sex; tool

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.

项目概要 急性呼吸困难（呼吸急促）是急诊就诊的最常见原因之一， 每年住院治疗。心力衰竭、肺炎和慢性阻塞性肺疾病是最常见的 常见病因，2017 年美国有 250 万人住院。确定确切原因 急性呼吸困难的诊断至关重要，但也具有挑战性，因为出现症状、实验室检查和 影像学结果可能难以解释，特别是对于老年人和患有合并症的患者或 重病。多达30%的患者可能会出现诊断错误和不适当的治疗，这是 与较差的患者预后相关。人工智能（AI）工具已被提议来增强 诊断过程中的提供者，并有能力支持急性疾病的诊断评估 呼吸困难。然而，不准确的人工智能工具也会降低临床医生的表现。因此，只需保持 临床医生的“在环”并不能保证对表现不佳的模型起到后盾作用。该提案寻求 实现临床医生与人工智能的有效合作，提高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评估急性呼吸困难人工智能工具，同时评估收集策略 临床医生的反馈，以实现持续的模型改进。我们的多学科团队由专家组成 临床医学、计算机视觉、机器学习和人机交互非常适合 应对这些重要挑战。该提案的成功完成将产生一个稳健的、可推广的 急性呼吸困难 AI 工具可增强医生对急性呼吸困难的诊断评估。更广泛地说， 该提案将带来可推广的知识，以支持跨领域的人工智能工具的更安全的开发和集成 医疗保健设置。