Oct 2026· npj Digital Public Health· Vol 1· 0 citations· 94 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.
Artificial intelligence (AI) is transforming disease surveillance by enabling early outbreak detection, predictive modeling, and real-time analysis of diverse health data sources. These advances can strengthen epidemic preparedness and improve public health decision-making. However, AI adoption has progressed faster th...
Abdullahi Osman Mohamed, A. Afyare, Abdinasir Mohamed Abdi et al.· Frontiers in Digital Health· 0 citations
A review of infectious disease surveillance platforms examined 20 platforms according to primary data inputs, surveillance function, AI methods, data privacy, geographic scope and pathogen focus and three archetypes emerged: Early Warning Networks, Situational Awareness Platforms and Integrated Surveillance Platforms.
Marya Getchell, Michael Barber, Tom Carpino et al.· npj Digital Medicine· 1 citation· ⚡1
Conflict early-warning systems have evolved significantly with the emergence of big data, artificial intelligence, and machine learning, offering new opportunities to anticipate violent conflict and support timely preventive interventions. This paper examines the use of big data in conflict early-warning, focusing on t...
K. Samuel J.· IDOSR Journal of Arts and Ma...· 0 citations
El Niño creates an important opportunity for anticipatory public health action because its development can often be detected before many downstream hazards emerge. Yet predictability does not transfer uniformly from the large-scale El Niño Southern Oscillation (ENSO) signal to regional climate anomalies and ultimately...
L. Bajrai, E. Azhar· Frontiers in Public Health· 0 citations
A structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems is presented, inspired by established methodologies in cybersecurity and national security.
T. Bauer, W. Kegelmeyer, Edmon Begoli et al.· 0 citations
Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to vari...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
