Development of AI-Driven Algorithms for Personalized Healthcare Using Genomic Data

Abstract

The integration of artificial intelligence (AI) and genomic data is reshaping customised medicine by allowing proper, data-driven medical treatment based on specific genetic data. The invention of the AI-based technology to examine complex genetic data to determine the presence of biomarkers, predict susceptibility to diseases, and recommend personalised treatment options is introduced in this work. The proposed system can successfully process high-dimensional genomic sequences and relate them to the clinical outcomes through the use of deep learning systems such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). In order to increase the accuracy of prediction, the models are trained with diverse genomic repositories through data preparation indicators such as feature selection and dimensionality reduction. A hybrid learning approach, which integrates supervised and unsupervised methods is utilized in order to discover latent patterns and gene-disease relationships. The high predictability of the model on the response of individuals to the treatments particularly in cancer therapy and rare genomic diseases is proven when compared to clinical population. The results show that AI in genomics can transform proactive, preventative and participative healthcare. To remain trustworthy and reliable, this paper highlights the importance of ethical data handling, transparency, and implementing AI technology in clinical practice. Ultimately, the developed algorithms symbolize an enormous step in the direction of a completely personalised medicine, where medical decisions will be made according to a specific genetic makeup of a patient.

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