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

点击翻译按钮获取中文摘要

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.