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“Artificial Intelligence Innovations In Substance Use Prevention: A Scoping Review” Metadata:

  • Title: ➤  Artificial Intelligence Innovations In Substance Use Prevention: A Scoping Review
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  • Internet Archive ID: osf-registrations-h637j-v1

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The Internet Archive:

This research project aims to explore how Artificial Intelligence (AI) and Machine Learning (ML) models are currently being applied to social media platforms for the prevention of substance use. The rapid expansion of social media has created a vast digital environment where substance-related content, peer influence, and behavioral patterns can be monitored and analyzed. AI/ML technologies have the potential to detect substance use trends, identify at-risk individuals, and optimize intervention strategies in real time. The primary objectives of this study are to (1) examine existing AI/ML applications in substance use prevention through social media analysis, (2) highlight challenges and opportunities associated with integrating AI/ML in digital public health initiatives. This research will synthesize evidence from recent studies, government reports, and AI-driven intervention programs to provide a comprehensive assessment of current advancements in the field. The expected outcomes include an in-depth understanding of how AI/ML models enhance prevention efforts, insights into ethical and privacy considerations, and recommendations for future AI-driven public health strategies. This study will contribute to the growing body of knowledge in digital health interventions and inform policymakers, healthcare professionals, and technology developers on best practice for leveraging AI/ML to curb substance use through social media platforms.

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"Artificial Intelligence Innovations In Substance Use Prevention: A Scoping Review" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.11 Mbs, and the file-s went public at Tue May 13 2025.

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  • Number of Files: 5
  • Number of Available Files: 5
  • Added Date: 2025-05-13 17:11:11
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