This narrative, practice-oriented review maps the 2024-2025 landscape of AI-enabled prescribing in opioid use disorder (OUD) care, where the evidence base is most developed, while noting cautious extension to other substance use disorders (SUDs). We link concrete clinical decisions to fit-for-purpose tools across four domains: EHR-embedded prediction and safety nudges; PDMP-derived analytics and risk scores; clinician-facing large language model (LLM) copilots; and prescription digital therapeutics, just-in-time adaptive interventions (JITAIs), and personal sensing systems. The regulatory analysis uses U.S. frameworks as worked examples and translates the same governance questions to European settings through the EU AI Act, MDR/IVDR medical software requirements, GDPR, the European Health Data Space, and the HMA-EMA data and AI workplan. Evidence maturity is uneven. EHR-based clinical decision support for naloxone co-prescribing and protocolized buprenorphine initiation has the most established implementation base. PDMP risk scores are clinically visible but incompletely validated and should remain advisory. Digital therapeutics and JITAI approaches may support engagement and retention, but they should not automate dosing. LLM copilots are most defensible as audited, citation-first conformance checkers rather than autonomous prescribers; patient-facing agents should be restricted to education, skills support, navigation, and escalation. Across settings, the highest-yield systems are transparent, narrowly scoped, workflow-native, and evaluated with prescribing-centered outcomes such as dose optimization, 28/90/180-day retention, naloxone co-prescribing and fills, and overdose-related safety. We propose safeguards that keep AI explainable, equitable, transferable across health systems, and subordinate to clinical judgment.

Background Internet Gaming Disorder frequently co-occurs with psychiatric conditions, yet many studies overlook these comorbidities, leading to oversimplified interpretations Objectives This study systematically examined thematic and interpretative differences in IGD research according to whether psychiatric comorbidities were explicitly incorporated. Method We analyzed 1,201 IGD-related articles, classified into With Comorbidity (WC; n = 21) and Without Comorbidity (WoC; n = 1,180) groups. After text preprocessing, BERTopic modeling was performed to identify and compare thematic structures. For the WoC group, topics were further classified as "psychiatric-related" if they contained at least two predefined psychiatric disorder keywords. For the WC group, the influence of comorbidity on outcome interpretation was evaluated. We implemented an AI-assisted Natural Language Processing pipeline to compare thematic structure across groups. Results Topic modeling identified 26 topics in WoC and 5 in WC. WoC topics covered diverse themes, whilst WC topics highlighted clinical interactions involving comorbidities such as ADHD, anxiety, and depression. In the WC group, 81% of studies showed that comorbidity significantly influenced outcome interpretations. To statistically compare the distribution of two groups, we performed centroid alignment with a correction factor based on the word-count ratio. Maximum mean discrepancy (MMD) analysis confirmed a significant difference in semantic distribution after correction (MMD distance=1.24*10^-4, p=0.02).Conclusions Explicit consideration of psychiatric comorbidities enhances the clinical and interpretative accuracy of IGD research. Future studies should systematically integrate comorbidity analyses to improve understanding and therapeutic management of IGD. The AI-assisted NLP synthesis offers a scalable evidence-mapping approach to identify clinically relevant gaps and biases in the IGD literature, particularly regarding psychiatric comorbidity.

In the article by Heckel et al. entitled "Variations in Regulated Cannabis Sales Conditions and Their Effect on Purchase Behaviour: Findings from an Observational Study" [Eur Addict Res. 2026;32(1):46-55; https://doi.org/10.1159/000551031], an error was introduced during production whereby the affiliation "Addictive Disorders Research Group" was mistakenly shown as a co-author. The name of the author Eva Maria Havelka was also mistakenly shown as "Eva-Maria Havelka."The original article has been updated.