Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Surfacing Suicidal Risk Through Simulated Social Interaction: Per-Person Language Model Agents as Communicative Stress Tests.

medRxiv : the preprint server for health sciences·2026

Same author

Initial Suicide-Related Disclosure Characteristics, Motivations, and Outcomes Based on Sexual Orientation.

Journal of clinical psychology·2026

Same author

Computational Modeling of Uncertainty and Volatility Beliefs in Escape-Avoidance Learning: Comparing Individuals with and Without Suicidal Ideation.

Journal of personalized medicine·2025

Same author

Initial Development of a Multidimensional Computerized Adaptive Test for Intensive Longitudinal Assessment of Suicide Risk: Development and Usability Study.

JMIR formative research·2025

Same author

Structural racism and suicide risk among Black youth: A systematic review.

Development and psychopathology·2025

Same author

Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment.

Psychological medicine·2025

Same journal

Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research.

JMIR mental health·2026

Same journal

Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study.

JMIR mental health·2026

Same journal

Using AI to Detect Psychosis Relapse: Scoping Review.

JMIR mental health·2026

Same journal

Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials.

JMIR mental health·2026

Same journal

Correction: Barriers and Facilitators in the Implementation of the Systematic Medical Appraisal, Referral, and Treatment (SMART) Mental Health Digital Intervention in Rural India: Mixed Methods Process Evaluation Study.

JMIR mental health·2026

Same journal

Examining the Social and Mental Health Benefits of Virtual and In-Person Physical Activity Intervention Among Postsecondary Students: Quasi-Experimental Study.

JMIR mental health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 10, 2026

$Subjective Refraction Test Using a Smartphone for Vision Screening$

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine

Ross Jacobucci¹, Wenpei Shao¹, Veronika Kobrinsky²

¹Center for Healthy Minds, University of Wisconsin-Madison, 625 W Washington Ave, Madison, WI, 53703, United States, 1 (608) 263-6321.

JMIR Mental Health

|April 8, 2026

Summary

This summary is machine-generated.

Analyzing smartphone screenshots with vision-language models (VLMs) can predict momentary suicidal ideation (SI) when personalized. This approach offers a promising, privacy-preserving tool for suicide prevention, complementing traditional methods.

Keywords:

digital phenotyping foundation models passive sensing phone use smartphone suicide

More Related Videos

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Apr 10, 2026

$Subjective Refraction Test Using a Smartphone for Vision Screening$

Subjective Refraction Test Using a Smartphone for Vision Screening

Published on: October 18, 2024

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Digital Health
Artificial Intelligence in Mental Health
Computational Psychiatry

Background:

Passive smartphone sensing offers potential for suicide prevention but lacks contextual data for acute distress detection.
Analyzing on-phone content (vision, text) may provide more direct indicators of psychological risk than usage patterns alone.

Purpose of the Study:

To evaluate the efficacy of vision-language models (VLMs) in predicting momentary suicidal ideation (SI) from smartphone screenshots.
To compare the performance of VLM-based prediction against text-only models and traditional lexical screening.

Main Methods:

Seventy-nine adults with recent suicidal thoughts/behaviors underwent 28-day monitoring with passive screenshot capture.
Fine-tuned open-source VLMs and text-only models to predict SI from screenshots preceding ecological momentary assessments (EMAs).
Evaluated model performance using temporal and subject holdout validation strategies.

Main Results:

VLM-based models demonstrated strong discrimination (AUC=0.83) at the EMA level, outperforming text-only models.
Subject-level generalization was limited (AUC≈0.50), indicating a need for personalization.
Smaller models showed comparable performance, suggesting feasibility for on-device deployment.

Conclusions:

Smartphone screen content analysis effectively predicts short-term SI with personalized models.
A two-stage clinical approach is proposed: initial lexical screening followed by personalized VLM monitoring.
On-device inference holds potential for privacy-preserving mental health monitoring.