This paper is in the following e-collection/theme issue:

Applications of AI (107) Wearable Devices and Sensors (911) New Methods (970) Mobile Health (mhealth) (2995) Artificial Intelligence (1454) Substance Abuse (431) mHealth for Data Collection and Research (987) Machine Learning (1518)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52270, first published .
Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Authors of this article:

Sang Won Bae1 Author Orcid Image ;   Tammy Chung2 Author Orcid Image ;   Tongze Zhang1 Author Orcid Image ;   Anind K Dey3 Author Orcid Image ;   Rahul Islam1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Sang Won Bae   1 , PhD ;   Tammy Chung   2 , PhD ;   Tongze Zhang   1 , MSc ;   Anind K Dey   3 , PhD ;   Rahul Islam   1 , BSc

1 Human-Computer Interaction and Human-Centered AI Systems Lab, AI for Healthcare Lab, Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology, Hoboken, NJ, United States

2 Institute for Health, Healthcare Policy and Aging Research, Rutgers University, Newark, NJ, United States

3 Information School, University of Washington, Seattle, WA, United States

Corresponding Author:

  • Sang Won Bae, PhD
  • Human-Computer Interaction and Human-Centered AI Systems Lab
  • AI for Healthcare Lab, Charles V. Schaefer, Jr. School of Engineering and Science
  • Stevens Institute of Technology
  • 1 Castle Point Terrace
  • Hoboken, NJ, 07030-5906
  • United States
  • Phone: 1 4122658616
  • Email: sbae4@stevens.edu