AI and Big Data approaches to addressing the opioid crisis: a scoping review protocol.

Journal: BMJ open
PMID: 39645274

Abstract

INTRODUCTION: This paper outlines the steps necessary to assess the latest developments in artificial intelligence (AI) as well as Big Data technologies and their relevance to the opioid crisis. Fatal opioid overdoses have risen to over 82 998 annually in the USA. This highlights the need for urgent and effective data-driven solutions. AI approaches, such as machine learning, deep learning and natural language processing, have been employed to analyse patterns and trends in overdose data and facilitate timely interventions. However, a comprehensive scoping review on the effectiveness of AI-driven technologies to detect, treat, prevent or respond to the opioid crisis remains absent. Thus, it is important to identify recent advancements in AI and Big Data technologies in addressing the opioid crisis.

Authors

  • Maaz Amjad
    Steve Hicks School of Social Work, The University of Texas at Austin, Austin, Texas, USA.
  • Scott Graham
    Rhetoric & Writing, The University of Texas at Austin, Austin, Texas, USA.
  • Katie McCormick
    Steve Hicks School of Social Work, The University of Texas at Austin, Austin, Texas, USA.
  • Kasey Claborn
    Steve Hicks School of Social Work, The University of Texas at Austin, Austin, Texas, USA kasey.claborn@austin.utexas.edu.

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