Effects of AI and Explainable AI in Myocardial Infarction Detection

Last registered on October 06, 2025
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Pre-Trial

Trial Information

General Information

Title
Effects of AI and Explainable AI in Myocardial Infarction Detection
RCT ID
AEARCTR-0016742
Initial registration date
October 02, 2025

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
October 06, 2025, 11:44 AM EDT

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

Locations

Country
China
Region

Primary Investigator

Name
Ziqing (Violet) Li
Affiliation
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Other Primary Investigator(s)

PI Name
Cathy (Liu) Yang
PI Affiliation
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PI Name
Kevin Bauer
PI Affiliation
Contact Investigator
PI Name
Xitong Li
PI Affiliation
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-10-08
End date
2025-12-10
Keywords
Behavior, Health
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines the influence of artificial intelligence (AI) and explainable AI (XAI) on physicians’ diagnostic decision-making in the context of myocardial infarction (MI) detection based on ECG signals. In a two-phase experiment, physicians and medical students first diagnose a set of ECG cases without AI support (baseline). They then diagnose additional cases with predictions from a state-of-the-art AI system, with random assignment to receive either standard AI predictions (control) or AI predictions with explanations (XAI treatment). The primary outcome is diagnostic accuracy and alignment with AI advice. Secondary outcomes include more performance-related measures, workflow efficiency and self-reported well-being. We also examine potential heterogeneous treatment effects and potential mechanisms, such as changes in risk perceptions and responsibility allocation, using survey measures. This design allows us to compare diagnostic performance with and without AI, as well as identify the incremental effect of XAI over standard AI support.
External Link(s)

Registration Citation

Citation
Bauer, Kevin et al. 2025. "Effects of AI and Explainable AI in Myocardial Infarction Detection." AEA RCT Registry. October 06. https://doi.org/10.1257/rct.16742-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This study employs a mixed-design experimental approach, incorporating both between-subjects and within-subjects factors to evaluate the intervention.

The intervention is the provision of AI-based decision support during the diagnostic task in Round 2.

Between-Subjects Factor: Participants are randomly assigned to one of two intervention conditions:
- AI Group: Receives the AI’s predicted probability of MI.
- XAI Group: Receives the AI’s predicted probability plus an explainable visualization (Grad-CAM highlights).

Within-Subjects Factor: All participants complete the diagnostic task without any decision support in Round 1 (baseline) and then again with their assigned decision support in Round 2 (treatment). This allows for a direct comparison of each participant's performance against their own baseline, measuring the change attributable to the intervention.
Intervention (Hidden)
Intervention Start Date
2025-10-08
Intervention End Date
2025-12-10

Primary Outcomes

Primary Outcomes (end points)
1. Diagnostic accuracy
2. Alignment with AI advice
Primary Outcomes (explanation)
1. Diagnostic accuracy will be constructed by comparing each participant’s categorical diagnosis (MI vs. normal) with the case ground truth. Correct judgments are coded as 1, incorrect as 0.
2. Alignment with AI advice will be measured as the absolute difference between the AI’s predicted probability of MI (scaled 0–1 or 0–100) and the physician’s assessed probability of MI for the same case. Smaller absolute differences indicate closer alignment with the AI’s estimate. In the case of XAI, alignment is also measured by the percentage of ECG leads chosen that are also highlighted by XAI.

Secondary Outcomes

Secondary Outcomes (end points)
1. Performance-related measures:
False positive rate.
False negative rate.
Precision.
Recall.
AUC(Area Under Curve).

2. Efficiency-related measures:
Decision to refer for additional tests (binary).
Decision to consult another clinician (binary).
Time taken for each diagnosis

3. Physician well-being:
Burnout, measured via the abbreviated Maslach Burnout Inventory (numeric score).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We conduct a mixed-design randomized controlled experiment to study how AI and explainable AI (XAI) affect physicians’ and medical students’ diagnostic decision-making. All participants first complete a baseline round diagnosing ECG cases without AI support. They are then randomized into one of two groups and complete a second round with either AI predictions (control) or AI predictions with explanations (XAI treatment).

The design allows for both within-subject comparisons (baseline vs. with AI support) and between-subject comparisons (AI vs. XAI). Primary outcomes are diagnostic accuracy and alignment with AI advice; secondary outcomes include more performance-related measures, efficiency and well-being measures. Survey measures capture potential mechanisms such as perceived risk, responsibility, and confidence in AI.
Experimental Design Details
Randomization Method
Randomization done in qualtrics
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0
Sample size: planned number of observations
approx. 130
Sample size (or number of clusters) by treatment arms
approx. 50 per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Gemeinsame Ethikkommission
IRB Approval Date
2025-10-01
IRB Approval Number
N/A
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials