How Efficient Are Neural Networks and AI Applications? A Review of Advanced Applications and Emerging Trends

Engineering notes

By evaluating state-of-the-art architectures and application-specific implementations, the study identifies pressing challenges, including environmental sustainability and data privacy, alongside opportunities for improvement.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The exponential growth of neural networks and artificial intelligence (AI) has revolutionized diverse fields, including healthcare, finance, natural language processing, and autonomous systems. These technologies have redefined the boundaries of what is possible, enabling solutions to complex problems across multiple domains. This review critically examines the efficiency of neural networks and AI applications in advanced settings, with a focus on computational performance, scalability, energy consumption, and ethical implications. By evaluating state-of-the-art architectures and application-specific implementations, the study identifies pressing challenges, including environmental sustainability and data privacy, alongside opportunities for improvement. Furthermore, it highlights emerging trends such as Green AI, federated learning, and neurosymbolic AI that are shaping the future of the field. This comprehensive analysis aims to provide actionable insights for researchers and practitioners seeking to optimize AI systems for greater effectiveness and impact. This review uses a structured literature review approach to examine approximately 50 sources, including journal articles, conference papers, preprints, and selected technical reports published up to 2026. The scope is limited to efficiency-oriented neural network and AI applications in healthcare, autonomous systems, natural language processing, finance, and environmental/climate science. The main contribution is a cross-domain comparison of algorithms, efficiency metrics, deployment settings, and open research gaps. The review finds that no single technique is universally efficient: CNNs and vision transformers remain strong for perception tasks, transformer and retrieval-augmented models dominate language applications, graph neural networks and reinforcement learning support relational and decision-making tasks, and compression, federated learning, edge deployment, and hardware acceleration are increasingly required to control latency, energy consumption, and scalability costs.

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