Oct 2026· npj Digital Public Health· Vol 1· 0 citations· 89 references
TL;DR
This work proposes reframing early warning as the perceptual foundation of AI-native decision intelligence for public health governance, and introduces ACCESS—integrating situational sensing, cognitive reasoning, scenario simulation, collective coordination, explainable accountability, and adaptive evolution.
Abstract
Epidemic early warning has been framed mainly as prediction, yet sophisticated forecasts often fail to translate into timely, coordinated action. We propose reframing early warning as the perceptual foundation of AI-native decision intelligence for public health governance, and introduce ACCESS—integrating situational sensing, cognitive reasoning, scenario simulation, collective coordination, explainable accountability, and adaptive evolution. This conceptual framework prioritizes decision robustness, transparency, and human accountability over predictive accuracy alone.
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
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