Human Algorithm Interactions for Acute Respiratory Failure Diagnosis

Acute Respiratory Failure Clinical Trial

Official title:

Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06098950
• Other study ID #: HUM00180745
• Secondary ID: R01HL158626
• Status: Completed
• Phase: N/A
• Start date: April 1, 2022
• Est. completion date: January 31, 2023

Study information

• Verified date: October 2023
• Source: University of Michigan
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Artificial intelligence (AI) shows promising in identifying abnormalities in clinical images. However, systematically biased AI models, where a model makes inaccurate predictions for entire subpopulations, can lead to errors and potential harms. When shown incorrect predictions from an AI model, clinician diagnostic accuracy can be harmed. This study aims to study the effectiveness of providing clinicians with image-based AI model explanations when provided AI model predictions to help clinicians better understand the logic of an AI model's prediction. It will evaluate whether providing clinicians with AI model explanations can improve diagnostic accuracy and help clinicians catch when models are making incorrect decisions. As a test case, the study will focus on the diagnosis of acute respiratory failure because determining the underlying causes of acute respiratory failure is critically important for guiding treatment decisions but can be clinically challenging. To determine if providing AI explanations can improve clinician diagnostic accuracy and alleviate the potential impact of showing clinicians a systematically biased AI model, a randomized clinical vignette survey study will be conducted. During the survey, study participants will be shown clinical vignettes of patients hospitalized with acute respiratory failure, including the patient's presenting symptoms, physical exam, laboratory results, and chest X-ray. Study participants will then be asked to assess the likelihood that heart failure, pneumonia and/or Chronic Obstructive Pulmonary Disease (COPD) is the underlying diagnosis. During specific vignettes in the survey, participants will also be shown standard or systematically biased AI models that provide an estimate the likelihood that heart failure, pneumonia and/or COPD is the underlying diagnosis. Clinicians will be randomized see AI predictions alone or AI predictions with explanations when shown AI models. This survey design will allow for testing the hypothesis that systematically biased models would harm clinician diagnostic accuracy, but commonly used image-based explanations would help clinicians partially recover their performance.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 457
• Est. completion date: January 31, 2023
• Est. primary completion date: January 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Physicians, nurse practitioners, and physician assistants that care for patients with acute respiratory failure as part of their clinical practice

Exclusion Criteria:

• Physicians, nurse practitioners, and physician assistants that only provide patient care in outpatient settings

Related Conditions & MeSH terms

• Acute Respiratory Failure
• Respiratory Distress Syndrome
• Respiratory Insufficiency

Intervention

Other:
• Artificial Intelligence model predictions without explanation
During 6 clinical vignettes, participants will see AI model predictions without a corresponding AI explanation. The AI model will provide a score for each diagnosis (heart failure, pneumonia, COPD) on a scale of 0-100 estimating how likely the patient's presentation was due to each of these diagnoses. In 3 of the clinical vignettes, participants will be shown standard AI model predictions and 3 vignettes they will be shown systematically biased AI model predictions, with the model specifically biased against one of the three diagnoses.
• Artificial intelligence model predictions with explanation
During 6 clinical vignettes, participants will see AI model predictions with explanation. The AI model will provide a score for each diagnosis on a scale of 0-100. In 3 clinical vignettes, participants will be shown standard AI model predictions and 3 vignettes they will be shown systematically biased AI model predictions with the model specifically biased against one of the three diagnoses. If the AI model provides a score above 50 an AI model explanation will be shown as gradient-weighted class activation mapping (Grad-CAM) heatmaps overlaid on the chest X-ray that highlighted which regions of the image most affecting the AI model's prediction.
• AI model biased against heart failure
In 3 clinical vignettes, participants will be shown systematically biased AI model predictions with the model specifically biased against heart failure, always predicting that heart failure is present with high likelihood in survey vignette patients with a body mass index (BMI) at or above 30. Standard predictions will be shown for the other 2 diagnoses (pneumonia, COPD).
• AI model biased against pneumonia
In 3 clinical vignettes, participants will be shown systematically biased AI model predictions with the model specifically biased against pneumonia, always predicting that pneumonia is present with high likelihood in survey vignette patients 80 years or older. Standard predictions will be shown for the other 2 diagnoses (heart failure, COPD).
• AI model biased against COPD
In 3 clinical vignettes, participants will be shown systematically biased AI model predictions with the model specifically biased against COPD, always predicting that COPD is present with high likelihood in survey vignette patients where a pre-processing filter was applied to the patient's X-ray. Standard predictions will be shown for the other 2 diagnoses (heart failure, pneumonia).

Locations

Country	Name	City	State
United States	University of Michigan	Ann Arbor	Michigan

Sponsors (2)

Lead Sponsor	Collaborator
University of Michigan	National Heart, Lung, and Blood Institute (NHLBI)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Participant diagnostic accuracy across clinical vignette settings	Diagnostic accuracy is defined as the number of correct diagnostic assessments over the total number of diagnostic assessments. After reviewing each individual patient clinical vignette within the survey, participants will be asked to make three separate diagnostic assessments for each clinical vignette, one for heart failure, pneumonia, and COPD. If the participant's assessment agrees with the reference label for each vignette, the diagnostic assessment is considered correct. Diagnostic assessments will be performed while participants are completing the survey (day 0), immediately after the participant reviews the clinical vignette. Participant diagnostic accuracy will be compared across vignette settings (no AI model, standard AI model, standard AI model with explanation, biased AI model, biased AI model with explanation).	Day 0
Secondary	Treatment Selection Accuracy across clinical vignette settings	Treatment selection accuracy is defined as whether the participant choose the correct treatment for the patient in the clinical vignette, and could choose any combination of steroids, antibiotics, Intravenous (IV) diuretics, or none of these treatments for the patient. Treatment selection assessments will be performed while participants are completing the survey (day 0), immediately after the participant reviews the clinical vignette. Participant treatment selection accuracy will be compared across vignette settings (no AI model, standard AI model, standard AI model with explanation, biased AI model, biased AI model with explanation).	Day 0
Secondary	Diagnosis specific diagnostic accuracy across clinical vignette settings	Diagnostic accuracy specific to heart failure, pneumonia, and COPD across vignette settings	Day 0