Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature
Bibliographic record
Abstract
BACKGROUND: With the artificial intelligence (AI) paradigm shift comes momentum toward the development and scale-up of novel AI interventions to aid in opioid use disorder (OUD) care, in the identification of overdose risk, and in the reversal of overdose. OBJECTIVE: As OUD-specific AI interventions are relatively recent, dynamic, and may not yet be captured in the peer-reviewed literature, we conducted a review of the gray literature to identify literature pertaining to OUD-specific AI interventions being developed, implemented and evaluated. METHODS: Gray literature databases, customized Google searches, and targeted websites were searched from January 2013 to October 2019. Search terms include: AI, machine learning, substance use disorder (SUD), and OUD. We also requested recommendations for relevant material from experts in this area. RESULTS: This review yielded a total of 70 unique citations and 29 unique interventions, which can be sub-divided into five categories: smartphone applications (n = 12); healthcare data-related interventions (n = 7); biosensor-related interventions (n = 5); digital and virtual-related interventions (n = 2); and 'other', i.e., those that cannot be classified in these categories (n = 3). While the majority have not undergone rigorous scientific evaluation via randomized controlled trials, several AI interventions showed promise in aiding the identification of escalating opioid use patterns, informing the treatment of OUD, and detecting opioid-induced respiratory depression. CONCLUSION: This is the first gray literature synthesis to characterize the current 'landscape' of OUD-specific AI interventions. Future research should continue to assess the usability, utility, acceptability and efficacy of these interventions, in addition to the overall legal, ethical, and social implications of OUD-specific AI interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".