Artificial Intelligence in Substance Use Prevention, Treatment, and Recovery Support: Selected Readings and Practical Insights
Artificial intelligence (AI) is transforming how we understand and manage substance use disorders (SUDs). The following curated readings highlight current developments, practical applications, and ethical considerations for AI in this field.
Screening & Prediction
These articles explore how generative AI can help identify risks, predict substance use behaviours, and guide early intervention efforts.
1. Substance Use Prediction Using Artificial Intelligence Techniques
This study utilises deep-learning models to analyse Finnish national survey data and forecast substance use patterns, illustrating how AI can enhance early detection.
🔗 Link
2. Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults
This article demonstrates that integrating AI-based screening into hospital workflows can reduce readmissions for patients with opioid use disorder while improving treatment access, efficiency, and cost-effectiveness.
🔗 Link
Digital/AI-Driven Interventions in Prevention, Treatment & Recovery Support
These readings explore how digital tools, chatbots, and AI-driven systems are used in prevention, treatment, behavioural change, relapse prevention, and recovery support.
3. Artificial Intelligence in Addiction: Challenges and Opportunities
This article provides an accessible overview of how AI is beginning to influence the field of addiction, including substance use disorders and behavioural addictions. It highlights the global burden of addiction, the treatment gap in many countries, and the urgent need for scalable solutions. The authors review emerging applications of AI in screening, diagnosis, intervention, and service delivery, while also noting limited progress relative to other areas of mental health. The piece underscores both the potential and the challenges of integrating AI into prevention and treatment systems, especially in resource-limited contexts.
🔗 Link
4. A Systematic Review of Chatbot-Assisted Interventions for Substance Use
This study is a systematic review of chatbot technologies in prevention, assessment and treatment for alcohol, nicotine and drug use. It offers valuable insights into the development and validation of chatbot-assisted interventions, thereby establishing a robust foundation for their efficacy.
🔗 Link
5. Digital Interventions Targeting Excessive Substance Use and Related Harms
This study is a scoping review of digital interventions for excessive substance use and substance use disorders. It shows that established approaches such as web- and text-based tobacco cessation, internet-based screening/brief interventions, and online Cognitive Behavioural Therapy (CBT) for alcohol generally reduce substance use at levels comparable to face-to-face care. It notes that evidence remains limited or mixed for illicit-drug–focused interventions, as well as for newer digital formats, including mobile apps, social media programs, videoconferencing, sensors, virtual reality, chatbots, and AI.
🔗 Link
6. Exploring the Potential and Challenges of Digital and AI-Driven Psychotherapy for Substance Use Disorders and Other Mental Health Disorders
This comprehensive narrative review underscores the strong potential of digital therapies, particularly AI-driven psychotherapy, in treating a range of mental health disorders. To maximise their benefits, integration should be guided by patient-centred care and careful attention to ethical considerations.
🔗 Link
7. Revolutionising Addiction Medicine: The Role of Artificial Intelligence
The article provides a comprehensive review of how AI is transforming addiction medicine through improved diagnosis, personalised treatment, and monitoring of patient progress. It also highlights ongoing challenges, such as data privacy, algorithmic bias, and the need for ethical guidelines to ensure AI enhances, rather than replaces, human clinical judgment.
🔗 Link
Ethical Considerations
As AI becomes more integrated into healthcare, ensuring its ethical and equitable use is crucial. It is essential to highlight the promise of digital and AI-driven therapies in mental health care while emphasising the need for patient-centred and responsible implementation.
8. Ethics of Artificial Intelligence in Medicine
The article explores the significant moral challenges that arise when integrating AI into healthcare, including concerns over transparency, accountability, and potential bias in decision-making. It emphasises the need for ethical frameworks that ensure AI systems support human autonomy, promote fairness, and maintain trust between patients and healthcare providers.
🔗 Link
Webinars
9. This ISSUP webinar entitled: "Reimagining Prevention: The Role of Generative AI" offers accessible discussions with Dr. Brian Klass, Assistant Director for Technology at the Johns Hopkins Bloomberg School of Public Health's Center for Teaching and Learning.
The webinar introduces generative AI and Large Language Models (LLMs) through simple explanations and hands-on activities such as summarising articles and developing AI-based prevention strategies. It shows how AI can support substance use prevention while addressing misinformation and bias.
10. The U.S. Food and Drug Administration (FDA) held a daylong Digital Health Advisory Committee session on “Generative Artificial Intelligence-Enabled (GenAI) Digital Mental Health Medical Devices”. This session covered FDA perspectives on generative AI–enabled digital mental health devices, regulatory considerations, and the evolving role of large language models in clinical tools. Discussions included GenAI’s impact on device development, developer insights, clinical trial evaluation, cross-sector lessons learned, patient-provider considerations, payer viewpoints, and health technology assessment perspectives.
