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

Development and Evaluation of Research Methods, Instruments and Tools (439) Mobile Health (mhealth) (2777) Formative Evaluation of Digital Health Interventions (2226) Substance Abuse (383) Opioid and Related Substance Misuse (128) Artificial Intelligence (1216)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45434, first published .
An Artificial Intelligence–Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study

An Artificial Intelligence–Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study

An Artificial Intelligence–Based Smartphone App for Assessing the Risk of Opioid Misuse in Working Populations Using Synthetic Data: Pilot Development Study

Authors of this article:

A B M Rezbaul Islam1 Author Orcid Image ;   Khalid M Khan2 Author Orcid Image ;   Amanda Scarbrough2 Author Orcid Image ;   Mariah Jade Zimpfer2 Author Orcid Image ;   Navya Makkena1 Author Orcid Image ;   Adebola Omogunwa2 Author Orcid Image ;   Sheikh Iqbal Ahamed3 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Al-hammuri K, Gebali F, Kanan A. ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Medical Errors in the Era of Generative AI and Cloud-Based Health Information Ecosystems. AI 2024;5(3):1111 View

Books/Policy Documents

  1. Rissanen A, Rissanen M. Health Information Science. View