A Review of the Application of Artificial Intelligence Across Engineering Disciplines
Keywords:
Artificial intelligence, Biomedical engineering, Generative design, Machine learning, Predictive analyticsAbstract
This review examines the application of Artificial Intelligence (AI) across major engineering disciplines, including mechanical, civil, electrical, industrial, computer, energy, healthcare, agricultural, environmental, chemical, aerospace, and biomedical engineering. Using a structured narrative review, the study synthesizes findings from research published between 2020 and 2025. The review shows that AI, particularly machine learning, deep learning, reinforcement learning, and computer vision, has improved design optimization, predictive maintenance, fault detection, and system automation across these fields. However, the extent of adoption differs because of variations in data availability, risk tolerance, validation requirements, and regulatory constraints. The review also discusses key challenges, including data quality, model interpretability, integration with legacy systems, and workforce readiness. Emerging developments such as explainable AI, quantum-enhanced simulation, and human-AI collaboration are identified as promising directions for future research and practice. By providing a comparative overview of current progress, limitations, and opportunities, this paper offers a practical framework for the responsible integration of AI into engineering practice.
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