Foundational Artificial Intelligence and Machine Learning Methodologies Most Relevant to Biomedical Research: A Comprehensive Review
Abstract Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming biomedical research by enabling high-precision pattern recognition, prediction, and data integration across imaging, omics, clinical records, and molecular datasets. Foundational methodologies—particularly deep learning architectures, natural language processing (NLP), and computer vision—have become indispensable in modern biology and medicine. This review comprehensively analyzes the principles, applications, and limitations of these methodologies, highlights their role in genomics, drug discovery, medical imaging, and diagnostics, and outlines future directions such as foundation models, multimodal architectures, and generative AI for biodesign. The review provides a consolidated understanding of how AI tools support hypothesis generation, accelerate translational research, and reshape precision medicine. 1. Introduction Biomedical research is undergoing a paradigm shift fueled by t...