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This work presents an automated framework that classifies suspected overdoses from death investigation reports, while producing supporting explanations guided by expert knowledge, and shows that a pipeline model with explanations grounded in the input death investigation reports outperforms the other settings.
Human-centered artificial intelligence models that identify suspected overdose deaths using narrative reports from medicolegal death investigations and describe ethical considerations for using AI in overdose surveillance are developed and evaluated.
Trace analysis shows failures concentrate at language-mediated reasoning - entity disambiguation, cross-source grounding, rather than tool invocation mechanics, rather than tool invocation mechanics.
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