Large Language Models in the Assessment and Care of Internet Gaming Disorder

Internet Gaming Disorder (IGD), recognized in the International Classification of Diseases (ICD-11), affects millions—especially adolescents and young adults—and poses challenges that invite scalable innovations in care. This narrative review examined how Large Language Models (LLMs) could support IGD prevention, assessment, treatment, and research. We conducted targeted searches of PubMed, Scopus, Google Scholar, and IEEE Xplore for 2010–October 2025, supplemented by backward/forward citation chasing. English, peer-reviewed clinical, methodological, and review work was prioritized. As a narrative review, we did not apply PRISMA or perform quantitative synthesis. In total, we synthesized over 50 sources. We analyzed peer-reviewed, IGD-specific AI/ML studies with explicit reporting of training approaches, validation/performance, dataset size, and model openness. While preliminary improvements have been observed in digital-health trials for depression, anxiety, and substance use, we emphasize that no IGD-specific LLM therapeutic trials exist to date; thus, evidence regarding treatment efficacy discussed here is extrapolated from these adjacent disorders. Evidence spans transformer-embedding text screening (r ≈ 0.48) and multimodal classification using EEG or fNIRS (accuracy ≈ 71–88%). Sample sizes ranged from 40 to 417 participants. Notably, most implementations remain research-only, lacking public code or data. Principal concerns include privacy and data governance, algorithmic bias, inconsistent crisis-escalation performance, and a nascent clinical evidence base. We conclude that LLMs may augment—but should not replace—human clinicians; future potential lies in hybrid human–AI pathways, multimodal integrations with wearables and gaming APIs, and rigorous prospective trials to establish safety, effectiveness, and equity in IGD care.