Abstract
The application of artificial intelligence (AI) in medical research has paved the way for the discovery of novel disease biomarkers, enhancing early diagnosis, prognosis, and personalized treatment strategies. AI-powered methods, such as machine learning and deep learning, enable the analysis of large-scale biomedical datasets, including genomic, proteomic, and metabolomic data, to identify biomarkers associated with diseases. This article reviews the current AI approaches used in biomarker discovery, with a focus on how these technologies are transforming precision medicine. We also explore the challenges and future directions in integrating AI with clinical practice to improve disease detection and therapeutic outcomes.
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