The role and potential of generative AI in meat processing technology innovation

The emergence of generative artificial intelligence (AI) presents new opportunities for innovation in the meat processing industry, which has traditionally relied on labor-intensive and manually controlled operations. This review explores the potential of generative AI—including models such as GANs, VAEs, LLMs, and MLLMs—in transforming various aspects of meat processing, from quality prediction and process simulation to automated documentation and decision-making. By integrating generative AI with sensor data, imaging systems, and cloud-based platforms, meat processors can enhance predictive accuracy, streamline operations, and reduce waste through virtual testing and real-time optimization. Case studies illustrate the application of generative AI in simulating defects, forecasting spoilage, synthesizing training data, and summarizing production records. Additionally, the paper discusses key considerations such as ethical responsibility, food safety compliance, system transparency, and environmental sustainability. Although technical challenges remain—including domain-specific model training, system integration, and regulatory validation—generative AI holds significant promise in advancing intelligent and sustainable meat processing systems. Future research should focus on scalable deployment, human-AI collaboration, and interdisciplinary frameworks to guide responsible implementation. This review highlights the transformative potential of generative AI to reshape the meat industry through smarter, data-driven innovation.