AI-Driven Personalized Healthcare Plans Using Genomic and Clinical Data in the Cloud
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I1P104Keywords:
Artificial Intelligence, Personalized Healthcare, Genomics, Clinical Data, Cloud Computing, Machine Learning, Predictive AnalyticsAbstract
The convergence of Artificial Intelligence (AI), cloud computing, and genomics is redefining contemporary healthcare. The emergent abundance in genomic and clinical data has opened a chance to develop individualized healthcare regimens based on a patient and his/her genetic constitution as well as his/her health background. This paper shows a widely applicable model of an AI-based individualized healthcare system and proposes a solution based on cloud-computing infrastructure where both the genomic and clinical data can be combined. It analyzes the importance of AI in interpreting large and complicated biomedical data and how it is used in predictive modeling, personalization of treatments and also in risk prediction. Reactive to proactive healthcare is made possible by using machine learning algorithms to decipher the genomes and understand clinical records. Some of the main issues, such as data privacy, model explainability, and computational needs, are addressed in the framework of cloud computing, which is characterised by scalable storage services, real-time processing, and universal accessibility. The present article outlines the research advances gathered before 2020 that created a robust foundation of the existing advancements and suggests a coherent approach to healthcare personalization on the basis of neural networks, clustering techniques, and supervised learning procedures. It also discusses experimental outcomes achieved through simulation using a real dataset. Lastly, in the paper, the direction of future personalized medicine was described as well as the ethical consequences of AI-assisted diagnosis and treatment
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