R. Gregory Lande, DO, COL (Ret) FACN, FAOAAM is a past president of the AOAAM. Dr. Lande retired from the US Army Medical Corps in recognition for which he received the Legion of Merit award. Dr. Lande then assumed a variety of administrative, clinical, academic, and research positions. Currently, Dr. Lande is a clinical supervisor, Editor-in-Chief of the Journal of Addictive Diseases, and a clinical professor at the Orlando College of Osteopathic Medicine. He is the author of over 100 articles, chapters, and books on a wide variety of medical and medical history subjects.
Artificial intelligence (AI) is rapidly transforming sectors such as business, military, and healthcare, offering innovative solutions for efficiency, diagnostics, and decision-making. However, its integration into addiction medicine has lagged other fields, despite the potential to address critical challenges in prevention, treatment, and recovery support. This gap stems from a combination of factors, including concerns over data privacy, ethical considerations, and the complexity of behavioral health. Even so, AI presents unique opportunities to serve as a collaborative tool that can bridge these gaps and advance addiction care. The Journal of Addictive Diseases is now calling for submissions that explore how AI can be harnessed to transform addiction medicine, emphasizing both its limitations and its promise.
Identifying critical research gaps
The priority is to identify critical research gaps in the application of AI within addiction medicine. While feasibility studies have laid the groundwork, they often lack the rigor needed to assess long-term outcomes or compare different AI-driven interventions. For instance, there is a pressing need for effectiveness studies that evaluate whether AI tools, such as predictive analytic models for the risk of return to use or chatbots for recovery support, translate into tangible improvements in patient care. Comparative analyses of AI applications across settings, such as outpatient clinics versus hospital systems, can also inform best practices and resource allocation.
A research agenda for AI in addiction medicine
To address these gaps, a structured research agenda is essential. This should prioritize questions that span the addiction care continuum: prevention, treatment, and recovery support. For example, how can AI enhance early detection of substance use disorders through pattern recognition in behavioral or epidemiologic data? What role can machine learning play in personalizing treatment plans for individuals with complex co-occurring conditions, such as suggesting treatment interventions? Similarly, AI tools could support recovery by providing real-time monitoring, such as using wearable devices, and suggest intervention strategies to prevent return to use.
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