Eyes Don’t Lie: Indifferent to AI-Generated Depression Screenings

This study explored how users respond to a depression self-diagnosis app with two outcomes: “doctor-generated” and “AI-generated.” The doctor-generated result was based on participants’ actual PHQ-9 depression scores, while the AI-generated result was randomly selected and sometimes matched the true diagnosis. Participants ( n = 47) with depressive symptoms used the app while pupil dilation was recorded using a Tobii Pro Spectrum eye tracker. Pupil dilation was analyzed before and during the decision phase. Participants who chose the doctor-generated result showed no difference in pupil dilation compared to those who chose AI. Our Extreme Gradient Boosting (XGB) model predicted trust with an AUROC of 0.871. These findings show that users engage equally with both AI and doctor-labeled diagnoses, but pupil diameter is indicative of their trust.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-07-16
DOI
https://doi.org/10.1177/10711813251357920
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Eyes Don’t Lie: Indifferent to AI-Generated Depression Screenings

Avishek Choudhury, Yeganeh Shahsavar
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Artificial Intelligence in Healthcare and Education
article

Eyes Don’t Lie: Indifferent to AI-Generated Depression Screenings

Avishek Choudhury, Yeganeh Shahsavar
article en

Abstract

This study explored how users respond to a depression self-diagnosis app with two outcomes: “doctor-generated” and “AI-generated.” The doctor-generated result was based on participants’ actual PHQ-9 depression scores, while the AI-generated result was randomly selected and sometimes matched the true diagnosis. Participants ( n = 47) with depressive symptoms used the app while pupil dilation was recorded using a Tobii Pro Spectrum eye tracker. Pupil dilation was analyzed before and during the decision phase. Participants who chose the doctor-generated result showed no difference in pupil dilation compared to those who chose AI. Our Extreme Gradient Boosting (XGB) model predicted trust with an AUROC of 0.871. These findings show that users engage equally with both AI and doctor-labeled diagnoses, but pupil diameter is indicative of their trust.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
West Virginia University (US)
West Virginia University
Openalex Percentile: Top 88%
Artificial Intelligence in Healthcare and Education
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