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Journals / Doğu Karadeniz Sağlık Bilimleri Dergisi / 2026 / Cilt: 5 Sayı: 1

AI in Addiction: A Social Work-Informed Approach from Risk Assessment to Intervention

Bağımlılıkta Yapay Zekâ: Risk Değerlendirmeden Müdahaleye Sosyal Hizmet Temelli Bir Yaklaşım

Serdarhan Duru(ANKARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ)0000-0002-2210-1684
Dilara Kaçan(ANKARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ ENSTİTÜSÜ)0009-0005-8532-2221
Pages
110–126
Publication Date
2026-03-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Yet structural risks also emerge, including privacy and dynamic consent concerns, algorithmic bias and stigma, covert surveillance, the digital divide, limited generalizability from small samples, and the relative neglect of behavioral addictions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines addiction via biopsychosocial and structural determinants and, from a social work perspective, positions AI-supported tools across risk assessment, monitoring/early warning, and psychosocial intervention.
📊
Key Findings & Data:It is a conceptual review of empirical and theoretical studies (2010–2025) on addiction, AI, and decision-support systems.
💡
Conclusion & Impact:Yet structural risks also emerge, including privacy and dynamic consent concerns, algorithmic bias and stigma, covert surveillance, the digital divide, limited generalizability from small samples, and the relative neglect of behavioral addictions.
🔬Design:Review & Systematic Survey
📍Scope:2010–2025 Verileri
⚙️Variables:Bağımlılık · Yapay Zekâ · Risk Değerlendirme

Abstract

This study examines addiction via biopsychosocial and structural determinants and, from a social work perspective, positions AI-supported tools across risk assessment, monitoring/early warning, and psychosocial intervention. It is a conceptual review of empirical and theoretical studies (2010–2025) on addiction, AI, and decision-support systems. The reviewed literature indicates that AI-supported tools may help combine clinical and social data to estimate risk and relapse, identify vulnerable groups through social media content and digital footprints, enable monitoring and alerts via wearables and mobile apps, personalize interventions through chatbots and digital therapeutics, and strengthen case management through hybrid models. Yet structural risks also emerge, including privacy and dynamic consent concerns, algorithmic bias and stigma, covert surveillance, the digital divide, limited generalizability from small samples, and the relative neglect of behavioral addictions.

Özet

Bu çalışma, bağımlılığı biyopsikososyal ve yapısal belirleyiciler üzerinden ele almakta ve sosyal hizmet perspektifiyle yapay zekâ destekli araçların bağımlılık alanında risk değerlendirme, izlem/erken uyarı ve psikososyal müdahale basamaklarındaki konumunu tartışmaktadır. Çalışma, bağımlılık, YZ ve karar destek sistemleri kesişiminde 2010–2025 yılları arasında yayımlanan ampirik ve kuramsal araştırmalara dayanan kavramsal bir derleme niteliğindedir. İncelenen literatür, yapay zekâ destekli araçların, risk ve nüksü tahmin etmek için klinik ve sosyal verileri birleştirmeye, sosyal medya içeriği ve dijital ayak izleri aracılığıyla savunmasız grupları belirlemeye, giyilebilir cihazlar ve mobil uygulamalar aracılığıyla izleme ve uyarılar sağlamaya, sohbet robotları ve dijital terapiler aracılığıyla müdahaleleri kişiselleştirmeye ve hibrit modeller aracılığıyla vaka yönetimini güçlendirmeye yardımcı olabileceğini göstermektedir. Bununla birlikte, mahremiyet ve rıza, algoritmik önyargı ve damgalama, örtük gözetim, dijital uçurum, küçük örneklemlerden kaynaklanan sınırlı genellenebilirlik ve davranışsal bağımlılıkların görece ihmal edilmesi gibi yapısal risk alanları öne çıkmaktadır.

🔖

Cite This Article

Duru, S., Kaçan, D. (2026). AI in Addiction: A Social Work-Informed Approach from Risk Assessment to Intervention. Doğu Karadeniz Sağlık Bilimleri Dergisi, Cilt: 5 Sayı: 1, 110–126. https://doi.org/10.59312/ebshealth.1886990

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