The Role of Artificial Intelligence in Analyzing Bioinformatics Big Data on Digital Health Platforms

The rapid expansion of next-generation sequencing and digital health infrastructure has produced vast volumes of genomic, transcriptomic, and proteomic data alongside the clinical data held in electronic health records. This conceptual review examines how artificial intelligence, particularly machine learning, deep learning, and natural language processing, is applied to analyze bioinformatics big data and integrate it with digital health platforms. The review shows that these methods support genetic variant interpretation, gene expression prediction, phenotype extraction from clinical notes, and personalized clinical decision support, improving diagnostic accuracy and clinical workflow. However, data heterogeneity, genomic privacy risks, limited interoperability between bioinformatics and electronic health record systems, and limited model transparency remain major obstacles. The paper concludes with recommendations for interoperable infrastructure and interdisciplinary training that combines health information technology with bioinformatics.

کلیدواژه ها:

Artificial Intelligence ، Bioinformatics ، Big Data ، Digital Health ، Machine Learning ، Electronic Health Records ، Personalized Medicine