Psychedelics for the Treatment of Substance Use Disorders: A Narrative Review of the Literature
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is an increasing interest in the use of psychedelics for the treatment of substance use disorders (SUDs) and to improve overall health and wellbeing. We aimed to update and complement research syntheses that have focused only on results from clinical trials by synthesizing the research across diverse methods to discuss implications from a broad and multi-faceted literature. METHODS: We conducted a narrative review of research focused on substance use and SUDs and both classic and non-classic/atypical psychedelics published between 1990 and 2025, synthesizing the evidence across population/survey studies, observational research, and clinical trials. RESULTS: There is growing investment in clinical research on psychedelics, yet methodological concerns within and across studies present challenges to the validity and generalizability of results. Research across population/survey studies, observational studies, as well as clinical trials suggest that symptom reduction is associated with a range of pharmacological, spiritual, and interpersonal processes. Findings from population surveys and observational studies align with growing clinical evidence in support of the use of psychedelics in the treatment of SUDs, indicating a need for more research to improve generalizability, understanding of safety concerns, the role of psychotherapies, and ethical implications of giving psychedelics to vulnerable populations. CONCLUSIONS: There is a need for more transparency in clinical research design and reporting, as well as for larger studies that track long-term outcomes. Studies with diverse methodological approaches are important to fill in knowledge gaps concerning treatment safety, tolerability, real-world effectiveness, accessibility, and respect for religious, traditional, and Indigenous communities. We specifically encourage more investment in observational, naturalistic, population-level, and survey research that can provide broad public health data on psychedelic-related SUD treatments and/or highly contextualized and historicized data on treatment methods, delivery, and experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".