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

Applications of AI (246) Wearable Devices and Sensors (1085) New Methods (1100) Mobile Health (mhealth) (3355) Artificial Intelligence (1996) Substance Abuse (515) mHealth for Data Collection and Research (1163) Machine Learning (1971)

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 (2) Tweetations Metrics

Journals

  1. Fitzer S, Ehlke S. Challenges and Insights of Cannabis Vaping Among Sexual Minority Individuals: Commentary on Maglalang et al. (2024). Substance Use & Misuse 2025;60(9):1411 View

Conference Proceedings

  1. Zhang T, Chung T, Dey A, Bae S. 2025 International Conference on Activity and Behavior Computing (ABC). AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use View