Patients’ Perceptions of AI Use in Health Care

Last registered on January 28, 2026

Pre-Trial

Trial Information

General Information

Title
Patients’ Perceptions of AI Use in Health Care
RCT ID
AEARCTR-0017724
Initial registration date
January 28, 2026

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 28, 2026, 8:02 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
University of Bologna

Other Primary Investigator(s)

PI Affiliation
University of Bologna
PI Affiliation
Johns Hopkins University

Additional Trial Information

Status
In development
Start date
2026-02-04
End date
2026-02-17
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Artificial intelligence (AI) tools are moving rapidly from back-office uses to health care to shape diagnoses, treatment choices, and follow-up recommendations. Yet adoption depends not only on whether AI can improve accuracy, but also on whether patients view the use of AI in health care as trustworthy, competent, and appropriately accountable, and whether those perceptions translate into concrete behavioral responses. This study uses randomized clinical vignettes to estimate how patients perceive physicians’ use of AI across common medical applications. The goal is to quantify the impact of AI use and of how it is framed on patients’ willingness to follow recommendations, their propensity to request second opinions, their trust in clinicians and institutions, and the perceived quality of health care. The goal is to identify which design and communication choices can preserve trust while enabling safe, effective integration of AI into medical decision-making.
External Link(s)

Registration Citation

Citation
Binelli, Chiara, Mario Macis and Laura Sartori. 2026. "Patients’ Perceptions of AI Use in Health Care." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.17724-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-02-04
Intervention End Date
2026-02-17

Primary Outcomes

Primary Outcomes (end points)
Primary outcomes for Vignette 1 and Vignette 2:
1. Probability to follow the recommendation of the doctor
2. Probability to request a second opinion from a different doctor

Primary outcomes for A/B Experiment:
1. Probability to follow the recommendation of the doctor versus probability to follow recommendation of AI
3. Probability to request a second opinion from a different doctor
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We have designed an online survey that will include two randomized clinical vignettes and one A/B experiment. Vignette 1 presents a routine chronic care scenario (diabetes' medication adjustment), and Vignette 2 presents a high-stakes specialist decision (oncology treatment plan). In both vignettes, respondents are randomly assigned to either a control condition with no mention of AI or a treatment condition in which AI is described as an additional verification step to the doctor's diagnosis.
The survey also includes an A/B experiment in which respondents are shown a scenario where the doctor and AI disagree on the recommended action. In one arm, the doctor advises the patient to follow the doctor's recommendation; in the other arm, the doctor seeks the opinion of a human colleague who agrees with the AI recommendation.
Experimental Design Details
Not available
Randomization Method
Randomization done by survey company
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
There are no clusters.
Sample size: planned number of observations
2,000
Sample size (or number of clusters) by treatment arms
Vignette A and B: 500 individuals control, 500 individuals treatment
A/B experiment: 1,000 individuals in scenario A, 1,000 individuals in scenario B
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number