AI in Healthcare: Predicting Epidemics Using AI and Data Analytics

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@article{1707654,
      author = {Sai Santhosh Polagani},
      title = {AI in Healthcare: Predicting Epidemics Using AI and Data Analytics},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {1183-1197},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707654.pdf},
      abstract = {The current epidemic outbreak management along with prediction relies heavily on Artificial Intelligence (AI) and data analytics processes. Epidemiological models succeed yet struggle to work with real-time information handling along with detection of early outbreaks and pattern identification. AI models that utilize machine learning and deep learning together with big data analytics bring considerable improvements to epidemic forecasting structures and early warning capabilities and real-time infectious disease surveillance processes. The various combination of electronic health records and social media trends alongside climate data and genomic sequencing through AI enables more effective disease monitoring as well as spread prediction and resource management. The implementation of AI technology in epidemic prediction produced successful results by forecasting COVID-19 outbreaks and tracking Ebola outbreaks along with predicting influenza future trends. The identification of outbreak patterns together with infection trajectory estimation depends on three machine learning algorithms which include LSTM networks and Support Vector Machines and Random Forests. Experimental findings show that AI-based forecasting systems enhance outbreak detection capability to an extent of 90% higher than epidemiological prediction frameworks. AI-driven epidemic forecasting algorithms encounter various obstacles such as data secrecy problems as well as software prejudice effects in combination with lacking standardized rules for AI use in healthcare across the globe. The enhancement of AI as a global epidemic preparedness tool depends heavily on solving these present challenges. The research investigates modern developments along with real-world examples and future predictions and obstacles regarding AI-based epidemic forecasting that simultaneously transforms public health management operations.},
      keywords = {AI in Epidemic Prediction, Machine Learning for Disease Forecasting, Big Data Analytics in Public Health, Real-Time Surveillance and AI, Predictive Modeling for Infectious Diseases},
      month = {March},
  }