Human Experts and Artificial Intelligence: The Value of Human Input in Diagnostic Imaging

Last registered on January 14, 2022

Pre-Trial

Trial Information

General Information

Title
Human Experts and Artificial Intelligence: The Value of Human Input in Diagnostic Imaging
RCT ID
AEARCTR-0008799
Initial registration date
January 13, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 14, 2022, 1:56 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
Massachusetts Institute of Technology

Other Primary Investigator(s)

PI Affiliation
Massachusetts Institute of Technology
PI Affiliation
Massachusetts Institute of Technology
PI Affiliation
Harvard Medical School

Additional Trial Information

Status
In development
Start date
2022-01-13
End date
2022-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We plan to investigate how human experts combine their own information with AI predictions when making assessments and decisions in the radiology domain. See the attached pre-analysis plan for full details.
External Link(s)

Registration Citation

Citation
Agarwal, Nikhil et al. 2022. "Human Experts and Artificial Intelligence: The Value of Human Input in Diagnostic Imaging." AEA RCT Registry. January 14. https://doi.org/10.1257/rct.8799
Experimental Details

Interventions

Intervention(s)
See the attached pre-analysis plan for full details.
Intervention Start Date
2022-01-14
Intervention End Date
2022-05-01

Primary Outcomes

Primary Outcomes (end points)
To measure the quality of diagnostic assessments and decisions we will primarily focus on the following primary outcomes variables for each pathology group.
1. Error in probability assessment
2. Incorrect treatment/followup recommendation

The primary pathology groups we will consider are:
1. Pooled outcomes for all pathologies
2. Pooled outcomes for all AI assisted pathologies
Primary Outcomes (explanation)
See the attached pre-analysis plan for full details.

Secondary Outcomes

Secondary Outcomes (end points)
1. Time-taken and measures of effort exerted to parse the information in the X-ray and the clinical history, with and without AI
2. Heterogeneity of treatment effects by pathology prevalence and AI performance
Secondary Outcomes (explanation)
See the attached pre-analysis plan for full details.

Experimental Design

Experimental Design
We ask radiologists to read chest x-rays, randomizing whether they have access to the patient's clinical history and an AI tool. See the attached pre-analysis plan for full details of the experiment and analysis plan.
Experimental Design Details
Not available
Randomization Method
The attached pre-analysis plan contains the full randomization details. The order in which radiologists go through the different treatments in each session will be randomized to account for order effects. For each radiologist, we will randomly sample 60 cases to be read under each experimental condition.2 We will then randomly select a sequence of images from the set of image sequences satisfying the following two criteria: (1) there are 15 cases in each treatment arm per round and (2) each image is read in all treatment arms across the rounds.

The advantage of the within-subject design is that we observe each radiologist making many decisions under each treatment arm, which makes it easier to detect effects as it can control for across-subject heterogeneity. In addition, this design facilitates estimation of an economic model of decision making described later on that is used to study automation bias or neglect.
Randomization Unit
Randomization occurs at the patient case level for each radiologist.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We expect between 30-35 radiologists to complete the experiment.
Sample size: planned number of observations
Each radiologist reads 60 cases under all four treatment arms. This will result in 8,400 total reads if 35 radiologists complete the experiment.
Sample size (or number of clusters) by treatment arms
Each radiologist will read 60 cases under each of the four treatment arms. This will result in 2,100 observations per arm if 35 radiologists complete the experiment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See the attached pre-analysis plan for full details of the power calculations.
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the User of Humans as Experimental Subjects
IRB Approval Date
2021-02-05
IRB Approval Number
E-2953
Analysis Plan

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