International Journal on Science and Technology

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Disease Prediction using Generative AI

Author(s) ASHUTOSH NARAIN GHOSH
Country India
Abstract In recent years, artificial intelligence (AI) has become a transformative force across various sectors, with healthcare standing as one of the most profoundly impacted. Among the myriad of AI subfields, Generative AI (GenAI) has emerged as a revolutionary approach capable of modeling complex, high-dimensional data for sophisticated prediction, synthesis, and decision-making tasks. This extended abstract explores the novel intersection of disease prediction and generative artificial intelligence, with an emphasis on how this confluence can improve diagnostic accuracy, early detection, personalized treatment plans, and public health monitoring.
Traditional diagnostic models in healthcare often rely on linear algorithms, statistical regression, or supervised learning paradigms, which necessitate vast amounts of labeled data and often suffer from limitations in generalizability, interpretability, and robustness to noisy or incomplete data. Generative AI, by contrast, leverages deep learning models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models to not only learn complex latent distributions from unstructured and structured medical data but also to simulate hypothetical patient profiles, generate synthetic datasets, and predict disease progression with high precision. The generative capacity of these models enables a deeper understanding of disease dynamics by learning probabilistic representations of physiological and pathological processes.
Field Computer Applications
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-06-13
DOI https://doi.org/10.71097/IJSAT.v16.i2.6187
Short DOI https://doi.org/g9qqwb
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