Addiction medicine explores the complex biological and psychological roots of substance use disorders, offering hope for more effective treatments and recovery strategies. This field examines how the brain adapts to drugs, the social factors that drive dependency, and the latest clinical approaches to helping individuals regain their lives. By focusing on evidence-based solutions, researchers aim to reduce harm and improve long-term outcomes for patients worldwide.

At Gist.Science, we curate the newest preprints from medRxiv dedicated to this vital area of study. Our team processes every new submission in this category, transforming dense academic findings into accessible plain-language explanations alongside detailed technical summaries. This dual approach ensures that both the general public and specialists can stay informed about the rapidly evolving science behind addiction treatment.

Below are the latest papers in addiction medicine, freshly summarized to help you understand the cutting-edge research shaping the future of recovery.

The emotional impact of gambling-related advertising: an experimental functional Near-Infrared Spectroscopy study protocol

This study protocol outlines a cross-sectional fNIRS experiment designed to investigate prefrontal cortical responses to gambling advertisements in individuals with varying gambling experiences, utilizing inter-subject correlation and multiband frequency analysis to identify neural markers of vulnerability and inform public health regulations.

Daniel, L.-I., Ros-Leon, A., Molina-Rodriguez, S., Pellicer-Porcar, O., Cabrera-Perona, V., Ibanez-Ballesteros, J.2026-05-27📄 addiction medicine

High-resolution Orbitofrontal Cortex Morphometry and Cannabis Use Disorder Severity in High-risk Emerging Adults: A Preliminary Study

This preliminary study of high-risk emerging adults reveals that greater cannabis use disorder severity is associated with reduced surface area and increased cortical thickness in specific orbitofrontal and medial prefrontal/anterior cingulate cortex subregions, which further correlate with depression, trauma symptoms, impulsivity, and specific cannabis use motives.

Hargreaves, T. L., McIntyre-Wood, C., Elsayed, M., Vandehei, E., Belisario, K. L., Lee, L., Blakely, A., Halladay, J. L., Amlung, M., Sweet, L. H., MacKillop, J.2026-05-27📄 addiction medicine

Global Burden Of Problematic Internet Use: An Umbrella Review and Metanalysis

This umbrella review and meta-analysis of 11 systematic reviews involving over 3 million individuals estimates the global prevalence of problematic internet use behaviors to range from 6% for gaming to 32% for smartphone use, while highlighting substantial methodological heterogeneity and a critical need for higher-quality, geographically diverse research.

Schwarze-Taufiq, T., Weber, S., Larrain, B., Gatica-Bahamonde, G., Corazza, O., Neicun, J., Stein, D. J., Ioannidis, K., Demetrovics, Z., Chamberlain, S. R., Carmi, L., Zohar, J., Rumpf, H.-J., Hall (…)2026-05-25📄 addiction medicine

Adiposity and inflammation mediate altered metabolic profiles in individuals with opioid use disorder

This study demonstrates that individuals with opioid use disorder exhibit altered metabolic profiles characterized by higher adiposity and inflammation, which sequentially mediate increased risks of lipid imbalance and elevated blood glucose levels compared to matched controls.

Li, X., Manza, P., Wang, G.-J., Giddens, N., Belcher, A., Schwandt, M., Diazgranados, N., Lynch, K. G., Volkow, N. D., Shi, Z., Wiers, C. E.2026-04-18📄 addiction medicine

A Machine Learning Based Causal Interface for Time-Varying Environmental Predictors of Substance Use Initiation in the ABCD Study

This study introduces a two-stage machine learning-based causal framework combining graph discovery and double machine learning to analyze longitudinal ABCD Study data, successfully identifying stable, time-varying environmental and behavioral predictors of substance use initiation to inform targeted prevention strategies.

Wei, M., Yadlapati, L., Peng, Q.2026-04-17📄 addiction medicine

Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI)

This study found that while machine learning models can detect smoking lapse risks, their modest overall performance and high inter-individual variability suggest that reducing assessment frequency or simplifying predictors does not consistently improve outcomes, indicating that such algorithms are best used in combination with rules-based approaches rather than as standalone solutions for just-in-time adaptive interventions.

Leppin, C., Brown, J., Garnett, C., Kale, D., Okpako, T., Simons, D., Perski, O.2026-02-27📄 addiction medicine

Estimating the Smallest Worthwhile Difference (SWD) of Psychotherapy for Alcohol Use Disorder: Protocol for a Cross-Sectional Survey

This protocol outlines a cross-sectional survey using the Prolific platform to estimate the smallest worthwhile difference (SWD) of psychotherapy for alcohol use disorder by assessing trade-offs between benefits and burdens among American respondents, with the goal of informing clinical decision-making and understanding variations across patient subgroups and professional stakeholders.

Sahker, E., Lu, I., Eddie, D., So, R., Luo, Y., Omae, K., Tajika, A., Angelo, J. P., Crisp, T., Coffin, B., Furukawa, T. A.2026-02-27📄 addiction medicine

Fighting Addictions, improving Lives through COmprehensive drug rehabilitation with music (FALCO): Protocol for an international randomised controlled trial

The FALCO study is a multinational, assessor-blinded randomized controlled trial involving 600 participants across seven countries that aims to evaluate the long-term efficacy of active music groups and music listening groups compared to treatment as usual in reducing addiction severity and improving recovery outcomes for individuals with substance use disorders.

Geretsegger, M., Meling, H. M. K., Savinova, A., Assmus, J., Dy, C. L., Mydland, T. S., Dybdahl, K., Johansen, B., Koelsch, S., Malerbakken, A., Sommerbakk, M., Tuastad, L., Erga, A. H., Hetland, J. (…)2026-02-23📄 addiction medicine