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

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. METHODS AND ANALYSIS: We will electronically search four scientific databases (PubMed, Web of Science, Engineering Village and PsycInfo), including finding reference lists and grey literature from 2013 to 2023. Covidence will be used for screening and selecting papers. We will extract information such as citation details, study context, data used, AI/Big Data technologies, features, algorithms and evaluation metrics. This data will be synthesised, analysed and summarised to draw meaningful conclusions and identify future directions to tackle the opioid crisis. ETHICS AND DISSEMINATION: Ethics approval is not required. Results will be disseminated via conference presentations and peer-reviewed publication.

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