Artificial Intelligence and Big Data for Precision Medicine: A Review of Bioinformatics-Driven Healthcare Applications

Abstract

Healthcare is in the middle of a quiet but profound shift. Genomic sequencers, hospital information systems, wearables and imaging archives now generate data faster than clinicians can read it, and that flood is reshaping what “evidence-based care” means. We review more than forty recent studies that bring artificial intelligence (AI), machine learning and big-data analytics into bioinformatics and precision medicine, spanning oncology, drug discovery, cardiology, neurology, public-health surveillance and healthcare operations. Reported accuracies and AUCs range from roughly 80% in early drug-discovery pipelines to above 94% in deep-learning-based pancreatic and breast imaging. Yet our reading also suggests a more cautious story: many models still suffer from limited external validation, opaque decision logic and uneven access to high-quality multi-omics data. We propose a layered conceptual framework that connects heterogeneous data sources, federated and privacy-preserving pre-processing, predictive and explainable AI engines, and downstream clinical applications. The paper closes with a discussion of remaining barriers, interpretability, fairness, regulatory uncertainty and workflow integration and outlines research directions for the next several years.