MIT Study: AI Health Tools Must Adapt to User Expertise

MIT tool predicts suicide risk from text lexicon

MIT researchers have built a lightweight, interpretable model that estimates suicide risk by scanning crisis texts for specific linguistic markers tied to 49 known risk factors. Unlike black-box LLMs, this system relies on a curated lexicon of roughly 60 terms per factor, allowing it to run locally and explain exactly which words drove its assessment. Validated against 16,000 Crisis Text Line conversations, it correctly identifies that mentions of lethal means and substance use are stronger predictors of imminent danger than general depression. This approach offers a privacy-preserving, transparent alternative for triaging mental health crises without requiring massive compute or sacrificing clinical interpretability.