Ignite Creation Date:
2024-05-06 @ 7:41 PM
Last Modification Date:
2024-10-26 @ 3:11 PM
Study NCT ID:
NCT06098950
Status:
COMPLETED
Last Update Posted:
2023-10-25
First Post:
2023-10-17
Brief Title:
Human Algorithm Interactions for Acute Respiratory Failure Diagnosis
Sponsor:
University of Michigan
Organization:
University of Michigan
Study Overview
Official Title:
Measuring the Impact of AI in the Diagnosis of Hospitalized Patients A Randomized Survey Vignette Multicenter Study
Status:
COMPLETED
Status Verified Date:
2023-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 models 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 patients presenting symptoms physical exam laboratory results and chest X-ray Study participants will then be asked to assess the likelihood that heart failure pneumonia andor 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 andor 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
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 patients presenting symptoms physical exam laboratory results and chest X-ray Study participants will then be asked to assess the likelihood that heart failure pneumonia andor 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 andor 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| R01HL158626 | NIH | None | httpsreporternihgovquickSearchR01HL158626 |