ReviewDrug and alcohol review2026
Is Addiction Research Addicted to Artificial Intelligence? Mapping the Intersection of Artificial Intelligence, Substance Use and Mental Health Through a Bibliometric Analysis.
Review in Drug and alcohol review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
issuesFrom extracting insights from large-scale, multimodal data to prevention and support, there is growing interest in the applications and implications of recent advances in Artificial Intelligence (AI) within the fields of addiction, substance use and mental health, which we refer to as ASUM. However, due to the absence of a structured mapping of AI for ASUM, it remains unclear 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 productivity over time, both in absolute terms and relative to its parent disciplines; (iii) the key topics within ASUM and their interrelations. KEY
findingsResults, supplemented by a comparison of similar fields, show that, while ASUM is an emerging and rapidly expanding domain (with a 25-fold increase in research output since 2012, attracting growing attention relative to parent disciplines as well as appearing to rely on applying more advanced AI methods than related fields), it remains largely fragmented through a dispersed group of infrequent contributors. IMPLICATIONS: An integration of the findings suggests two dominant trajectories through which AI for ASUM is currently being realised: as AI-driven analytic support and as innovative research and therapeutic methods (e.g., virtual reality, chatbots).
conclusionsThe paper concludes by situating AI for ASUM as an emerging scientific field, outlining the scientific and practical challenges and opportunities that are likely to arise, and high-potential research areas open for exploration.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.