Real-world deployment of machine learning models for opioid overdose and opioid use disorder: a systematic review of clinical and operational lessons for addiction medicine

Deployed ML for opioid outcomes is feasible but uneven in maturity. Clinical implications: addiction medicine clinicians should (1) request subgroup performance results before adoption; (2) confirm post-deployment monitoring plans for drift and calibration; and (3) use human-in-the-loop safeguards (clinician or human verification before automated actions) to reduce harms from false positives/negatives.