OBJECTIVES: To develop and evaluate human-centered artificial intelligence (AI) models that identify suspected overdose deaths using narrative reports from medicolegal death investigations and describe ethical considerations for using AI in overdose surveillance.
METHODS: We trained AI models using 2022–2023 data from the New York City Office of Chief Medical Examiner (OCME). Narrative death investigation reports (n = 9263) were analyzed using a 2-step pipeline that first extracted textual evidence for 18 expert-defined indicators of overdose and then predicted overdose using 2 large language model families: LLaMA-8B-Instruct and ClinicalModernBERT. Domain experts provided feedback throughout model development, and safeguards were implemented to protect confidentiality and evaluate algorithmic bias.
RESULTS: The LLaMA-8B-Instruct model achieved 88% positive predictive value (PPV), 92% sensitivity, 84% negative predictive value (NPV), 76% specificity, and 85% macro-F1. The ClinicalModernBERT model achieved 83% PPV, 92% sensitivity, 82% NPV, 67% specificity, and 81% macro-F1. Both AI models outperformed OCME’s existing surveillance tool and conventional logistic regression approaches.
CONCLUSIONS: Human-centered AI may support timely and accurate overdose surveillance, but responsible implementation requires expert oversight, a privacy-compliant infrastructure, and ongoing evaluation of potential bias.
Human-centered artificial intelligence framework for overdose mortality surveillance, New York City, 2022-2023
American Journal of Public Health, 116 (10), 1565-1574. doi: 10.2105/AJPH.2026.308698.