Affiliation:
1. O.P. JIndal Global University, India
2. Doon University, Dehradun, India
3. Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed), Pune, India & Samara State Medical University, Russia
4. University of Minnesota, Minneapolis, USA
Abstract
AI is rapidly transforming the field of epidemiology. This chapter explores how AI integrates data analysis, predictive modeling, disease surveillance, and diagnostic tools to significantly improve public health outcomes. AI-driven methodologies enhance diagnostic accuracy, improve disease surveillance efficiency, and aid in developing better predictive models, all of which contribute to improved public health strategies. AI seamlessly integrates with traditional epidemiological approaches, paving the way for a new era in combating infectious diseases. Advancements in AI hold immense promise for the future of public health, with possibilities for real-time disease surveillance, personalized medicine, and more accurate predictive modeling. However, broader adoption and responsible use of AI require careful consideration of ethical issues, data privacy concerns, and collaboration among stakeholders. Ultimately, leveraging AI effectively has the potential to improve public health outcomes, ensure equitable access to healthcare, and enhance global preparedness for health crises.
Reference15 articles.
1. Allami, R. H., & Yousif, M. G. (2023). Integrative AI-driven strategies for advancing precision medicine in infectious diseases and beyond: a novel multidisciplinary approach. arXiv preprint arXiv:2307.15228.
2. From machine learning to deep learning: An advances of the recent data-driven paradigm shift in medicine and healthcare.;C.Chakraborty;Current Research in Biotechnology,2023
3. AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
4. Uncovering hidden and complex relations of pandemic dynamics using an AI driven system
5. Translation of AI into oncology clinical practice