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Human Algorithm Interactions for Acute Respiratory Failure Diagnosis

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT06098950
Recruitment Status : Completed
First Posted : October 25, 2023
Last Update Posted : October 25, 2023
Sponsor:
Collaborator:
National Heart, Lung, and Blood Institute (NHLBI)
Information provided by (Responsible Party):
Michael Sjoding, University of Michigan

Brief 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.


Condition or disease Intervention/treatment Phase
Acute Respiratory Failure Other: Artificial Intelligence model predictions without explanation Other: Artificial intelligence model predictions with explanation Other: AI model biased against heart failure Other: AI model biased against pneumonia Other: AI model biased against COPD Not Applicable

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 457 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Intervention Model Description: While participating in a survey, participants are randomized to see different hypothetical patient clinical vignettes, AI model predictions, and then ask questions about the patient's likely diagnosis and treatment.
Masking: Single (Participant)
Masking Description: Participants are not aware of what type of AI model predictions are shown during the clinical vignettes within the survey.
Primary Purpose: Other
Official Title: Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study
Actual Study Start Date : April 1, 2022
Actual Primary Completion Date : January 31, 2023
Actual Study Completion Date : January 31, 2023


Arm Intervention/treatment
Experimental: AI model biased for heart failure, no AI explanation
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against heart failure, always predicting that heart failure is present with high likelihood in patients with a body mass index (BMI) at or above 30. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will not be shown an AI explanation when shown AI model predictions.
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.

Other: 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).

Experimental: AI model biased for pneumonia, no AI explanation
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against pneumonia, always predicting that pneumonia is present with high likelihood in patients 80 years or older. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will not be shown an AI explanation when shown AI model predictions.
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.

Other: 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).

Experimental: AI model biased for COPD, no AI explanation
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against COPD, always predicting that COPD is present with high likelihood when a pre-processing filter was applied to the patient's X-ray. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will not be shown an AI explanation when shown AI model predictions.
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.

Other: 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).

Experimental: AI model biased for heart failure, Image-based AI explanation presented
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against heart failure, always predicting that heart failure is present with high likelihood in patients with a body mass index (BMI) at or above 30. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will also be shown AI explanation when shown AI model predictions.
Other: 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.

Other: 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).

Experimental: AI model biased for pneumonia, Image-based AI explanation presented
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against pneumonia, always predicting that pneumonia is present with high likelihood in patients 80 years or older. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will also be shown AI explanation when shown AI model predictions.
Other: 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.

Other: 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).

Experimental: AI model biased for COPD, Image-based AI explanation presented
Participants in this arm will be shown standard AI model predictions during 3 patient clinical vignettes within the survey and systematically biased AI model predictions during 3 clinical vignettes. When shown systematically biased AI model predictions, the model will be biased against COPD, always predicting that COPD is present with high likelihood when a pre-processing filter was applied to the patient's X-ray. Standard predictions will be shown for the other 2 diagnoses. Participants in this arm will also be shown AI explanation when shown AI model predictions.
Other: 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.

Other: 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).




Primary Outcome Measures :
  1. Participant diagnostic accuracy across clinical vignette settings [ Time Frame: Day 0 ]
    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).


Secondary Outcome Measures :
  1. Treatment Selection Accuracy across clinical vignette settings [ Time Frame: Day 0 ]
    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).

  2. Diagnosis specific diagnostic accuracy across clinical vignette settings [ Time Frame: Day 0 ]
    Diagnostic accuracy specific to heart failure, pneumonia, and COPD across vignette settings



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06098950


Locations
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United States, Michigan
University of Michigan
Ann Arbor, Michigan, United States, 48103
Sponsors and Collaborators
University of Michigan
National Heart, Lung, and Blood Institute (NHLBI)
Investigators
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Principal Investigator: Michael Sjoding, MD University of Michigan
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Responsible Party: Michael Sjoding, Associate Professor of Internal Medicine, University of Michigan
ClinicalTrials.gov Identifier: NCT06098950    
Other Study ID Numbers: HUM00180745
R01HL158626 ( U.S. NIH Grant/Contract )
First Posted: October 25, 2023    Key Record Dates
Last Update Posted: October 25, 2023
Last Verified: October 2023
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: Data could be made available to other researchers from accredited research institutions after entering into a data use agreement with the University of Michigan
Supporting Materials: Study Protocol
Statistical Analysis Plan (SAP)
Time Frame: Data will be shared indefinitely once the study is published
Access Criteria: This information will be published as supplements with the study manuscript.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Michael Sjoding, University of Michigan:
Artificial Intelligence
Diagnostic Accuracy
Computer Assisted Diagnosis
Biased Model
Additional relevant MeSH terms:
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Respiratory Insufficiency
Respiratory Distress Syndrome
Respiration Disorders
Respiratory Tract Diseases
Lung Diseases