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

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $665,592

## Abstract

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.

## Key facts

- **NIH application ID:** 10909198
- **Project number:** 5R01HL158626-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Michael William Sjoding
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $665,592
- **Award type:** 5
- **Project period:** 2021-09-20 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909198

## Citation

> US National Institutes of Health, RePORTER application 10909198, Human-AI Collaborations to Improve Accuracy and Mitigate Bias in Acute Dyspnea Diagnosis (5R01HL158626-04). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/10909198. Licensed CC0.

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