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Issues: From extracting insights from large-scale, multimodal data to prevention and support, there is growing interest in theapplications and implications of recent advances in Artificial Intelligence (AI) within the fields of addiction, substance use andmental health, which we refer to as ASUM. However, due to the absence of a structured mapping of AI for ASUM, it remainsunclear how this interest is translated into concrete research results.

Approach: This paper addresses this gap by conducting a bibliometric analysis of AI for ASUM, exploring: (i) the scale of ASUM-related research (number of publications, authors, institutions and countries); (ii) the evolution of ASUM‘s research productivityover time, both in absolute terms and relative to its parent disciplines; (iii) the key topics within ASUM and their interrelations.

Key Findings: Results, supplemented by a comparison of similar fields, show that, while ASUM is an emerging and rapidly ex-panding domain (with a 25-fold increase in research output since 2012, attracting growing attention relative to parent disciplinesas well as appearing to rely on applying more advanced AI methods than related fields), it remains largely fragmented through adispersed group of infrequent contributors.

Implications: An integration of the findings suggests two dominant trajectories through which AI for ASUM is currently beingrealised: as AI-driven analytic support and as innovative research and therapeutic methods (e.g., virtual reality, chatbots).

Conclusions: The paper concludes by situating AI for ASUM as an emerging scientific field, outlining the scientific andpractical challenges and opportunities that are likely to arise, and high-potential research areas open for exploration.