Applications of machine learning in cannabis research: A scoping review

• Machine learning (ML) is increasingly applied in cannabis research for data analysis. • ML algorithms predict risks and factors contributing to cannabis use effectively. • ML tools enable effective monitoring and information extraction about cannabis. • Bias arises from cross-sectional, non-representative data and recall-based training. • Re -evaluating methods and validating ML models may improve research applicability. Over the past decade, research about cannabis and its associated compounds has increased substantially. Machine learning (ML) is increasingly used in cannabis-related research to improve data analysis and modeling. The present scoping review aimed to identify how ML is used in the context of cannabis research. A scoping review was conducted following Arksey and O'Malley's five-stage scoping review framework. MEDLINE, EMBASE, PsycINFO and CINAHL were systematically searched, and CADTH was searched using keywords. Studies utilizing ML in the context of cannabis research were deemed eligible. Title and abstract and full text screening, data extraction, thematic coding, and analysis were performed independently and in duplicate for all included studies. Forty-six studies were included. Four themes emerged: 1) the sampling methodologies utilized in studies investigating cannabis and ML introduce bias in results, 2) ML algorithms can predict characteristics associated with cannabis use, including predictive factors, risk of usage, and impact on users, 3) ML algorithms are an effective tool for monitoring and extracting information about cannabis; and 4) various ML algorithms were most suitable for different tasks. This scoping review highlights two major uses of ML algorithms in cannabis research—for predicting risks of and factors contributing to cannabis use, and for extracting information about cannabis. Challenges associated with ML in cannabis research included the introduction of bias in results from the use of cross-sectional and non-representative data, and recall bias which may have led to biased training of ML models. Re -evaluating study methodology suitability and externally validating ML models may increase the viability/applicability of ML in cannabis research.