The Impact of AI on Professional Decision-Making: Evidence from Medical Scenarios

Last registered on December 26, 2025

Pre-Trial

Trial Information

General Information

Title
The Impact of AI on Professional Decision-Making: Evidence from Medical Scenarios
RCT ID
AEARCTR-0017455
Initial registration date
December 11, 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
December 26, 2025, 2:04 AM 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

Other Primary Investigator(s)

PI Affiliation

Additional Trial Information

Status
In development
Start date
2025-12-15
End date
2026-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
AI is rapidly penetrating various professional fields, but it remains unclear how human experts respond to algorithmic suggestions. This study focuses on the core theoretical questions of human-robot collaboration: Are AI suggestions adopted as decision-making aids, or do they trigger defensive resistance among professionals?
We use medical scenarios as an empirical environment to test doctors' responses to AI diagnostic suggestions provided by patients through standardized patient (SP) field experiments.
External Link(s)

Registration Citation

Citation
Miao, Meng and Xingjian Wang. 2025. "The Impact of AI on Professional Decision-Making: Evidence from Medical Scenarios." AEA RCT Registry. December 26. https://doi.org/10.1257/rct.17455-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-01-01
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
Consistency of doctor's plans with AI recommendations
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This project plans to systematically investigate the influence mechanism of artificial intelligence diagnostic suggestions on doctors' professional decision-making through standardized patients (SP) field experimental methods. The study will be conducted in real medical scenarios to obtain behavioral data of experts collaborating with AI in their natural state.
The project will recruit a certain number of standardized patients through professional actor recruitment channels and community volunteer networks in a voluntary manner. All standardized patients will receive full project instructions and professional training before participation, and can participate in formal experiments only after passing the training.
The experiment will be conducted in the routine outpatient clinic of the hospital, and standardized patients will be treated according to a unified disease script (common diseases such as osteoporosis, prediabetes, hypertension, etc.). The experiment adopts a randomized group design: the control group standardizes patients without mentioning AI; Treatment group 1 presented AI diagnosis suggestions before the doctor's initial consultation; Treatment group 2 presented AI recommendations after the doctor's initial consultation. AI suggestions are divided into two categories: high-quality and controversial, with cross-random assignment.
Standardized patients fill in a structured record form immediately after the end of the visit, recording the doctor's diagnosis, prescription, examination recommendations, consultation duration, and key information of doctor-patient interaction, but standardized patients do not perform any real medical operations.
Experimental Design Details
Not available
Randomization Method
randomization done by computer algorithm
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 individuals
Sample size: planned number of observations
500 individuals
Sample size (or number of clusters) by treatment arms
200 individuals Treatment group 1, 200 individuals Treatment group 2, 100 individuals Control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Renmin University of China
IRB Approval Date
2025-12-09
IRB Approval Number
N/A