Abstract
The integration of biotechnology with Artificial Intelligence (AI) is revolutionizing therapeutic development by overcoming the high costs and long timelines of traditional drug discovery. As biological data grows in complexity, machine learning and deep learning have become essential for precise clinical decision-making. This study explores the synergy between big data, omics technologies, and computational biology, highlighting AI’s transformative role in medical imaging, genomic analysis, and point-of-care testing for early disease detection. In drug development, AI streamlines target selection, molecular docking, and ADMET prediction, significantly reducing R&D expenses. These advancements extend to personalized medicine, immunotherapy, and infectious disease management, including antimicrobial resistance prediction. Despite its potential to improve public health and lower costs, the field faces hurdles such as data quality issues, ethical concerns, and model interpretability. Addressing these challenges is vital for the seamless integration of AI-powered biotechnology into global clinical practice and biomedical research.
