ReviewEuropean addiction research2026
Artificial Intelligence-Enabled Prescribing in Opioid Use Disorder Care: Decision Support, Risk Scores, Large Language Model Copilots, and Digital Therapeutics (2024-2025).
Review in European addiction research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis narrative, practice-oriented review maps the 2024-2025 landscape of artificial intelligence (AI)-enabled prescribing in opioid use disorder care, where the evidence base is most developed, while noting cautious extension to other substance use disorders. SUMMARY: We link concrete clinical decisions to fit-for-purpose tools across four domains: electronic health record (EHR)-embedded prediction and safety nudges; prescription drug monitoring program (PDMP)-derived analytics and risk scores; clinician-facing large language model (LLM) copilots; and prescription digital therapeutics, just-in-time adaptive interventions (JITAIs), and personal sensing systems. The regulatory analysis uses US frameworks as worked examples and translates the same governance questions to European settings through the EU AI Act, MDR/IVDR medical software requirements, GDPR, the European Health Data Space, and the HMA-EMA data and AI work plan. Evidence maturity is uneven. EHR-based clinical decision support for naloxone coprescribing and protocolized buprenorphine initiation has the most established implementation base. PDMP risk scores are clinically visible but incompletely validated and should remain advisory. Digital therapeutics and JITAI approaches may support engagement and retention, but they should not automate dosing. KEY MESSAGES: LLM copilots are most defensible as audited, citation-first conformance checkers rather than autonomous prescribers; patient-facing agents should be restricted to education, skills support, navigation, and escalation. Across settings, the highest-yield systems are transparent, narrowly scoped, workflow-native, and evaluated with prescribing-centered outcomes such as dose optimization, 28/90/180-day retention, naloxone coprescribing and fills, and overdose-related safety. We propose safeguards that keep AI explainable, equitable, transferable across health systems, and subordinate to clinical judgment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.