The article explores how artificial intelligence (AI) is currently used — and could be used — in the field of addiction research and treatment. The authors note that AI (machine learning, data mining, predictive analytics) offers substantial promise to enhance our understanding of substance use disorders (SUDs), support early detection, improve personalised interventions, and potentially improve outcomes for people with addiction. 

At the same time, the paper carefully outlines multiple challenges and limitations that must be addressed:

• Data quality and representativeness: Many existing...

Background

The COVID-19 emergency has tested the public health, medical, and public safety infrastructure of the United States (U.S.) in unprecedented ways. As the focal point for public health in their communities, local health departments (LHDs) were particularly impacted by the pandemic’s onset. The pandemic limited the scale and scope of local health department (LHD) work, redirecting resources to the response. However, the need for essential public health services—including substance use prevention—was not reduced.

Methods

Six quantitative data sources were examined, collected between...

Background 

In the United States, access to buprenorphine remains low and disparities regarding who receives treatment have emerged. Federal laws have regulated buprenorphine delivery, ultimately limiting its implementation more broadly. At the onset of the COVID-19 pandemic, federal agencies acted quickly to remove a legal barrier, effectively allowing people with opioid used disorder (OUD) to initiate buprenorphine treatment via telemedicine. Leveraging this policy shift, a low barrier buprenorphine treatment initiative via telemedicine was started at syringe service programs in California...