ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND MEDICINE

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare by addressing the complexity of human biology and vast data generated from multi-omics technologies. High-throughput ‘omics’ technologies. such as genomes, epigenomics, transcriptomics, proteomics, and metabolomics have transformed biological research by facilitating comprehensive molecular profiling of living systems. Current advancements in artificial intelligence, specially deep learning architectures like recurrent, convolutional, and graph neural networks, now facilitate the integration of several omics layers enabling early disease detection, accurate classification, and risk prediction across a range of conditions including Alzheimer’s disease, cancers, cardiovascular disorders, diabetes, tuberculosis, stroke, and skin and liver diseases. The cutting edge of this subject is combining ‘omics’ data with clinical and phenotypic data, which is often taken from electronic health records (EHRs). This integration helps find connections between changes in molecules and disease risk, progression, treatment response, and patient stratification. Bio-bank-scale datasets, in conjunction with single-cell and multi-modal ‘omics’ methodologies, provide higher resolution by incorporating variability in age, ethnicity, lifestyle, and cell type. Even with these improvements, significant challenges still persist. It can be hard to work with multi-omics datasets since they are generally high-dimensional, noisy, incomplete, and need a lot of computing power. Challenges such as model over-fitting, limited interpretability of complex algorithms and problems with cross-platform harmonization make it hard to use applications in a strong way. Ethical issues including privacy, fairness as well as problems with data storage, computation, and knowledge, also need to be fixed right away. Still, the possibilities are great, AI is advancing personalized medicine by identifying patient-specific biomarkers, predicting therapeutic response, and guiding individualized treatment strategies. AI holds the potential to transform diagnostics, prognostics, and therapeutic decision-making, paving the way toward precision healthcare.