Acute Respiratory FailureNCT06098950
Human Algorithm Interactions for Acute Respiratory Failure Diagnosis
A study testing Artificial Intelligence model predictions without explanation, Artificial intelligence model predictions with explanation and AI model biased against heart failure in people with Acute Respiratory Failure, run by University of Michigan at 1 site in United States.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Respiratory Failure
- What is being tested?
- Artificial Intelligence model predictions without explanation, Artificial intelligence model predictions with explanation and AI model biased against heart failure
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 457 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Apr 2022to 31 Jan 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Physicians, nurse practitioners, and physician assistants that care for patients with acute respiratory failure as part of their clinical practice
Who cannot join
- Physicians, nurse practitioners, and physician assistants that only provide patient care in outpatient settings
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study
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.
- Conditions
- Acute Respiratory Failure
- Being tested
- 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
- Keywords
- Artificial Intelligence, Diagnostic Accuracy, Computer Assisted Diagnosis, Biased Model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence model predictions without explanationOther
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 explanationOther
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 failureOther
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 pneumoniaOther
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 COPDOther
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).
| Study group | Role | What they receive |
|---|---|---|
| AI model biased for heart failure, no AI explanation | Experimental | 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. Receives: Other: Artificial Intelligence model predictions without explanation; Other: AI model biased against heart failure |
| AI model biased for pneumonia, no AI explanation | Experimental | 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. Receives: Other: Artificial Intelligence model predictions without explanation; Other: AI model biased against pneumonia |
| AI model biased for COPD, no AI explanation | Experimental | 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. Receives: Other: Artificial Intelligence model predictions without explanation; Other: AI model biased against COPD |
| AI model biased for heart failure, Image-based AI explanation presented | Experimental | 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. Receives: Other: Artificial intelligence model predictions with explanation; Other: AI model biased against heart failure |
| AI model biased for pneumonia, Image-based AI explanation presented | Experimental | 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. Receives: Other: Artificial intelligence model predictions with explanation; Other: AI model biased against pneumonia |
| AI model biased for COPD, Image-based AI explanation presented | Experimental | 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. Receives: Other: Artificial intelligence model predictions with explanation; Other: AI model biased against COPD |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Day 0
Secondary outcomes (2)
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).
Measured: Day 0
Diagnosis specific diagnostic accuracy across clinical vignette settings
Diagnostic accuracy specific to heart failure, pneumonia, and COPD across vignette settings
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Oct 2023
- Study start1 Apr 2022
- Primary completion31 Jan 2023
- Study completion31 Jan 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Acute Respiratory FailureStudies of the same condition, open and under way.
- More trials run by University of MichiganThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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 blind
- Who is masked
- Participant
- Enrolment
- 457 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Michigan
- Sponsor type
- University, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI)
- Responsible party
- Principal investigator, Michael Sjoding, University of Michigan
- Investigators
- Michael Sjoding, MD, Principal investigator, University of Michigan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06098950
- Sponsor's protocol ID
- HUM00180745
- Other IDs
- R01HL158626
- Status last verified
- Oct 2023
- First submitted
- 17 Oct 2023
- Last updated
- 25 Oct 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Data could be made available to other researchers from accredited research institutions after entering into a data use agreement with the University of Michigan
Publications
Jabbour S, Fouhey D, Shepard S, Valley TS, Kazerooni EA, Banovic N, Wiens J, Sjoding MW. Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study. JAMA. 2023 Dec 19;330(23):2275-2284. doi: 10.1001/jama.2023.22295.
PubMed 38112814
Study at a glance
From the registry record, last updated by the sponsor on 25 Oct 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Survey Vignette Multicenter Study
- ClinicalTrials.gov ID
- NCT06098950
- Acronym / sponsor ID
- HUM00180745
- Conditions
- Acute Respiratory Failure
- Keywords
- Artificial Intelligence, Diagnostic Accuracy, Computer Assisted Diagnosis, Biased Model
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 457 participants (actual)
- Lead sponsor
- University of MichiganUniversity, hospital or other
- Collaborators
- National Heart, Lung, and Blood Institute (NHLBI)
- Study start
- 1 Apr 2022 (actual)
- Primary completion
- 31 Jan 2023 (actual)
- Study completion
- 31 Jan 2023 (actual)
- First posted
- 25 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 6
- 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.
| Arm | Type | Receives |
|---|---|---|
| AI model biased for heart failure, no AI explanation | Experimental | Other: Artificial Intelligence model predictions without explanation, Other: AI model biased against heart failureParticipants 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. |
| AI model biased for pneumonia, no AI explanation | Experimental | Other: Artificial Intelligence model predictions without explanation, Other: AI model biased against pneumoniaParticipants 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. |
| AI model biased for COPD, no AI explanation | Experimental | Other: Artificial Intelligence model predictions without explanation, Other: AI model biased against COPDParticipants 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. |
| AI model biased for heart failure, Image-based AI explanation presented | Experimental | Other: Artificial intelligence model predictions with explanation, Other: AI model biased against heart failureParticipants 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. |
| AI model biased for pneumonia, Image-based AI explanation presented | Experimental | Other: Artificial intelligence model predictions with explanation, Other: AI model biased against pneumoniaParticipants 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. |
| AI model biased for COPD, Image-based AI explanation presented | Experimental | Other: Artificial intelligence model predictions with explanation, Other: AI model biased against COPDParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Physicians, nurse practitioners, and physician assistants that care for patients with acute respiratory failure as part of their clinical practice
Key exclusion criteria (1 listed)
- Physicians, nurse practitioners, and physician assistants that only provide patient care in outpatient settings
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence model predictions without explanationDuring 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 for COPD, no AI explanation, AI model biased for heart failure, no AI explanation, AI model biased for pneumonia, no AI explanation |
| Artificial intelligence model predictions with explanationDuring 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 for COPD, Image-based AI explanation presented, AI model biased for heart failure, Image-based AI explanation presented, AI model biased for pneumonia, Image-based AI explanation presented |
| AI model biased against heart failureIn 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). | Other | AI model biased for heart failure, Image-based AI explanation presented, AI model biased for heart failure, no AI explanation |
| AI model biased against pneumoniaIn 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). | Other | AI model biased for pneumonia, Image-based AI explanation presented, AI model biased for pneumonia, no AI explanation |
| AI model biased against COPDIn 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). | Other | AI model biased for COPD, Image-based AI explanation presented, AI model biased for COPD, no AI explanation |
Primary outcome
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).
2 secondary outcomes
Treatment Selection Accuracy across clinical vignette settings
Time frame: Day 0
Diagnosis specific diagnostic accuracy across clinical vignette settings
Time frame: Day 0
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Oct 2023.
- Derived from this study: PubMed 38112814Jabbour S, Fouhey D, Shepard S, Valley TS, Kazerooni EA, Banovic N, Wiens J, Sjoding MW. Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study. JAMA. 2023 Dec 19;330(23):2275-2284. doi: 10.1001/jama.2023.22295.
- Individual participant dataSharing planned: Yes. Data could be made available to other researchers from accredited research institutions after entering into a data use agreement with the University of Michigan
Related trials for Acute Respiratory Failure
Not yet recruiting
Modified Inhaled Nitric Oxide in Ventilated Patients With Acute Respiratory Failure
Acute Respiratory Failure (ARF), Nitric Oxide
Not yet recruitingObservational study
Impact of Inspiratory Effort and Patient-Ventilator Interaction During the Transition Phase
Mechanically Ventilated Patients, Acute Respiratory Failure (ARF)
Not yet recruitingObservational study
Development and Prospective Validation of the AutoEIT Foundation Model for ARDS
ARDS (Acute Respiratory Distress Syndrome), Respiratory Failure
Not yet recruitingObservational study
Diaphragm Response in Elderly for Acute Monitoring
Acute Dyspnea, Acute Respiratory Failure
RecruitingPhase 4
DECONGEST-AHF: Vasodilatation or Loop Diuretics in Acute Heart Failure
Pulmonary Edema, Congestive Heart Failure
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Michael Sjoding, MD principal investigator
University of Michigan
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence model predictions without explanation, Artificial intelligence model predictions with explanation and AI model biased against heart failure in people with Acute Respiratory Failure, run by University of Michigan at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Oct 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Participant diagnostic accuracy across clinical vignette settings (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06098950, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Oct 2023.