The Role of Artificial Intelligence in Modern Biomedical Research: From Data to Drugs

Abstract

The ability to efficiently analyze large-scale and complex biological data helps artificial intelligence to rapidly revolutionize biomedical research. This paper summarizes the value of artificial intelligence in three biomedical domains: multi-omics data integration for disease prediction, medical image analysis for clinical diagnose and drug discovery. In multi-omics research, artificial intelligence optimizes integration outcomes of heterogeneous biological datasets, deepening cognition of disease mechanisms and polishing predictive models. In medical imaging, deep learning performs excellently in various diagnosis tasks, yet the lack of interpretability brings out limitations. To address the problem, explainable artificial intelligence was generated to effectively increase model transparency and clinical validation. In drug discovery, artificial intelligence significantly accelerates main processes, including virtual screening, drug repurposing and protein structure prediction. In particular, AlphaFold is one of the most paramount breakthroughs. Despite these advancements, data heterogeneity, lack of clinical validation samples and model interpretability shortage restrain the practical usage of relevant techniques. This paper sorts out the potential and limitations of artificial intelligence in biomedical research, pointing out that future research should build more stable and interpretable models.