Clinicians' and people who use drugs' perspectives on artificial intelligence in addiction medicine

Vollständiger Abstract

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Abstract Background and aims Artificial intelligence (AI) may improve addiction medicine workflows and health outcomes but raises questions about data privacy and unintended harms for people who use drugs (PWUD). This qualitative study aimed to understand the perspectives of clinicians and PWUD regarding perceived benefits and concerns about clinical AI in addiction medicine. Design A qualitative study with one‐on‐one semi‐structured interviews about AI. Setting Addiction medicine clinicians in Rhode Island ( n = 7), Massachusetts ( n = 2) and Pennsylvania ( n = 3), USA. PWUD ( n = 25) residing in Rhode Island. Participants In 2025, a purposive sample of 12 clinicians and 25 PWUD participated. Eligible clinicians were English‐speaking, ≥18 years old, licensed and provided care to PWUD exposed to xylazine in Rhode Island, Massachusetts or Pennsylvania. PWUD recruited from four Rhode Island social service organizations were eligible if they reported past‐month illicit fentanyl and xylazine use, lived in Rhode Island or Massachusetts and were English‐speaking and ≥18 years old. Eight (67%) clinicians were women and 10 (83%) were white non‐Hispanic. Fifteen (60%) PWUD were men and 17 (68%) were white non‐Hispanic. Measurements Transcribed audio‐recorded interviews exploring benefits and concerns about clinical AI in addiction medicine. Clinician interviews were analyzed using rapid qualitative analysis to assess the breadth of clinical experience, whereas PWUD interviews were analyzed following inductive thematic analysis to assess depth of lived experience. Five themes were produced. Results Five themes were generated: (1) transparently developed and accurate clinical AI tools offer practical benefits; (2) clinician comfort with clinical AI hinges on transparent development and assurances against the perpetuation of drug‐related stigma; (3) clinical AI may not keep pace with the volatile and regionally specific drug supply; (4) PWUD have limited AI knowledge and some characterized AI as untrustworthy; and (5) PWUD are altruistically motivated to release medical data for clinical AI development but want control over identifiable data usage. Summary Clinicians and patients feel that artificial intelligence in addiction medicine holds promise, but they are concerned about clinical relevance, stigma perpetuation and the privacy of potentially incriminating data about illicit drug use.