From Detection to Behavioral Change: A Scoping Review of Natural Language Processing in Substance Use Research

Background

Natural language processing (NLP) has emerged as a promising approach for extractingclinically relevant information from unstructured text that would otherwise be difficult to capturethrough manual review alone. Although NLP in medical settings began in the 1960s, applications haveexpanded rapidly in the 2000s, with broader use of digital narrative data such as electronic healthrecords (EHRs), clinical notes, psychotherapy transcripts, and other language-based data sources. Insubstance use research, however, the literature on NLP applications remains fragmented across datasources, methods, and clinical domains, making it difficult to identify areas of convergence andimportant gaps. Accordingly, this scoping review examined NLP applications in substance use research,focusing on clinical screening and phenotyping, risk prediction, treatment outcomes, and psychotherapyprocess.