Predicting medical crises in Public Health with AI

Artificial intelligence (AI) is increasingly being applied to public health via early warning systems (EWSs) designed to predict medical crises, particularly infectious disease outbreaks or any disaster using real-time data. A recent systematic review found that AI-based EWSs effectively integrate multiple data streams—such as epidemiological surveillance, climate, web-based sources, wastewater, social media, sensor networks, mobile apps, and electronic health records—to detect outbreak signals earlier than conventional surveillance. Machine learning (ML), deep learning (DL), natural language processing (NLP) techniques, and predictive analytics are most commonly used, enabling models to parse both structured and unstructured data. For example, recent work on influenza forecasting employed a probabilistic deep-learning model (Dense ResNet) trained on surveillance data to provide continuous risk estimates several days in advance, outperforming binary-threshold systems. AI-driven systems do not supplant traditional surveillance; rather, they augment it by enabling earlier signal detection, which can trigger investigations, diagnostics, and public health interventions. However, substantial challenges remain: data quality, variability in data granularity, biases in input data, and integration into public health workflows. The use of AI in public health raises questions about privacy, consent, and accountability. The legal and ethical framework must evolve along with technology. Ethical dilemmas including privacy, equity, and the risk of false alarms including cyber security issues when analyzing sensitive personal data must be ensured.

The challenge is not only to collect data but to interconnect and analyze them accurately. Their value multiplies when institutions, research centers, and public health services collaborate, forming a vibrant information ecosystem. The challenge is quality, security, and collaboration of sources. Overall, peer-reviewed evidence suggests that AI-enhanced EWSs hold strong promise for transforming public health crisis prediction. Realizing this potential demands rigorous validation, transparent model design, and close collaboration between data scientists and public health professionals.