Data-Driven Insights for Health and Agriculture: Integrating Machine Learning with Microfluidics and Drug Screening

Abstract

This paper explores the synergistic integration of machine learning techniques with microfluidics and drug screening methodologies to enhance health and agriculture sectors. Microfluidics provides a platform for precise manipulation of fluids at the microscale, facilitating high-throughput experimentation and analysis. Coupled with machine learning algorithms, it offers unprecedented insights into complex biological systems. In health, this integration enables rapid and cost-effective drug discovery, personalized medicine, and disease diagnostics. In agriculture, it revolutionizes crop management, disease detection, and agrochemical optimization, thereby promoting sustainable practices. We discuss recent advancements, challenges, and future prospects of this interdisciplinary approach, emphasizing its potential to address critical issues and drive innovation in both domains.