Artificial Intelligence in Science and Technology: Applications, Challenges, and Future Perspectives

Abstract

Artificial Intelligence (AI) has emerged as a transformative force reshaping modern science and technology. Driven by advances in machine learning, deep learning, big data analytics, and high-performance computing, AI enables rapid data processing, predictive modeling, and intelligent decision-making across diverse disciplines. This paper examines the core technologies underlying AI and explores its applications in physics and materials science, chemistry and drug discovery, biology and healthcare, environmental science, and engineering systems. AI-driven approaches accelerate materials discovery, optimize simulation modeling, enhance medical imaging diagnostics, support genomic analysis, improve renewable energy management, and enable smart manufacturing and urban infrastructure. Despite these advancements, significant challenges remain, including data privacy concerns, algorithmic bias, limited explainability of complex models, high computational energy consumption, and regulatory gaps. Addressing these issues is essential for responsible and sustainable AI deployment. The study further discusses emerging directions, including generative AI, autonomous scientific discovery, quantum-enhanced machine learning, and sustainable AI frameworks. AI represents both a powerful enabler of scientific innovation and a domain requiring ethical oversight and interdisciplinary collaboration. A balanced approach integrating technological advancement with governance and sustainability will determine the long-term societal impact of Artificial Intelligence.