Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study
Trial ID: NCT06098950
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-01
Completion date: 2023-01-31
Sponsor: University of Michigan
Collaborators: National Heart, Lung, and Blood Institute (NHLBI)
Conditions: Acute Respiratory Failure
Interventions: Artificial Intelligence model predictions without explanation; Artificial intelligence model predictions with explanation; AI model biased against heart failure; AI model biased against pneumonia; AI model biased against COPD
Locations: University of Michigan
Countries: United States
Enrollment: 457
Age range: 18 Years - None
Sex: ALL

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.

Eligibility 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

Outcomes:
Participant diagnostic accuracy across clinical vignette settings