Generative artificial intelligence (genAI) plays a crucial role in improving AI-based digital twins (DTs), enabling more dynamic, adaptive, and accurate industrial simulations, essential as Industry 5.0/6.0 paradigms evolve and are implemented. In industry, genAI can simulate complex manufacturing processes or entire production lines, enabling companies to optimize operations, predict maintenance needs, reduce downtime, and develop more scenarios for correct operation (e.g., for faster transitions to new products or new materials) and address potential failures. GenAI also helps DTs continuously learn and evolve by generating new data and scenarios based on historical and current inputs. This capability ensures that DTs remain current and reflective of the real systems they represent, for both operational and training purposes (e.g., training operators for situations that rarely occur on a real production line).Furthermore, it facilitates the creation of synthetic data, which is important for training AI models when real-world data is scarce or expensive. This accelerates the development and improvement of DTs and increases the predictive accuracy, personalization, and operational efficiency of AI-based digital twins, making them more reliable and versatile tools in medicine and industry. However, in addition to strengths, it is also worth considering threats to prepare for risk mitigation. This article helps capture and maintain a balance between opportunities and threats in this area.
Rojek et al. (Tue,) studied this question.
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