<![CDATA[The integration of image processing and artificial intelligence (AI) is transforming business automation by enabling systems to interpret and act on visual data with human-like intelligence. This review explores the theoretical foundations and real-world applications of AI-driven image processing across industries such as manufacturing, healthcare, finance, logistics, and retail. Techniques like convolutional neural networks (CNNs), ResNet, YOLO, and Vision Transformers are used in tasks including defect detection, facial recognition, and document verification, yielding significant efficiency gains and cost reductions. Despite these benefits, challenges remain. These include a reliance on large annotated datasets, high computational demands (e.g., GPU costs), and limited model transparency. Ethical concerns such as bias in facial recognition and privacy issues in surveillance further complicate adoption. To address these, emerging solutions include the use of synthetic data (e.g., GANs), edge deployment for low-latency processing, and multimodal AI that combines image, text, and sensor inputs for deeper insights. Regulatory compliance with standards like GDPR and the EU AI Act is increasingly vital to ensure responsible use. This review presents a structured framework for integrating image processing with AI, outlining each stage from image acquisition to real-time decision-making and continuous learning. By highlighting current capabilities, limitations, and future trends, this paper encourages cross-industry collaboration and sustained RD investment to unlock the full potential of scalable, ethical, and intelligent automation in the age of Industry 4.0.]]>
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