Smart Disease Management: AI-Driven Approaches for Surveillance, Prediction, and Public Health Mitigation

Abstract

In the last ten years, artificial intelligence has changed public health by offering revolutionary techniques to illness surveillance, risk evaluation, and resource distribution. Public health workers are now better able to anticipate and respond to future epidemics thanks to AI-driven anticipating insights have been crucial in tracking increasing movements. To identify potential warning signs of disease outbreaks, machine learning algorithms scour a variety of data sets, comprising healthcare records, data on genes, networking posts, and surfing searches, among others. These algorithms can analyze large datasets faster and more accurately than classic epidemiological methods, producing results almost instantly. Chatbots, virtual assistants, and AI speed up remote testing and improve medical information access. Hospital admissions and expenditures decrease. Telemedicine uses AI to replace subjective medical assessments with evidence-based treatment suggestions from large databases. Thus, healthcare is less biased. Since technology can change the world, ethical implementations must follow rules, avoid algorithmic biases, and protect privacy. This study analyzes AI-driven telemedicine's potential to improve healthcare disparities and create a more modern and inclusive healthcare system for chronic conditions.

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