## 7 Conclusions  
AI has revolutionized IAM in industrial environments by providing advanced, adaptive, and intelligent solutions to enhance security and operational efficiency.  
For example, the integration of AI into IAM systems is set to significantly enhance security and operational efficiency within healthcare organizations. As healthcare continues its digital transformation, the protection of sensitive patient data has become a critical concern. AI-driven IAM solutions are particularly valuable in this context, as they help manage access control, ensure compliance with privacy regulations, and mitigate the risk of unauthorized access to medical records [87]. By leveraging machine learning, biometrics, and adaptive authentication methods, these systems can offer more secure, scalable, and efficient solutions compared to traditional IAM systems.  
While challenges remain, ongoing advancements in AI technologies and methodologies continue to drive the evolution of IAM systems, ensuring they can meet the growing security demands of modern industrial operations.  
In this paper, we have presented a comprehensive review of the current state-of-the-art AI technologies and their applications in industrial IAM systems conducted around three research questions.  
In response to the first RQ \"What are the main contributions of AI in identification and authentication with application in industrial IAM systems?\", the study concludes that AI has significantly advanced the identification and authentication capabilities of IAM systems through biometric recognition comprising facial and voice recognition, whose accuracy, reliability, and versatility have been improved by deep learning algorithms. These technologies improve the accuracy, reliability, and versatility of IAM systems in industrial environments.  
Next, after addressing the second RQ \"What are the advanced capabilities that AI can bring to IAM systems?\", the study shows that the most prominent advanced capabilities of AI-based IAM systems are real-time monitoring and anomaly detection, predictive analytics, adaptive authentication mechanisms, and user behavior analysis. These capabilities improve security, operational efficiency, and scalability in industrial environments, providing a more robust and adaptable IAM framework.  
Finally, as a result of the third RQ \"What are the main challenges and future lines of research?\", the study allows us to conclude that the challenges and solutions in implement ing AI-based IAM systems include the integration of explainable AI, federated learning, edge computing, and blockchain technology. These emerging trends and technologies are poised to transform the field of IAM, enhancing security, efficiency, and scalability in industrial environments.  
In conclusion, based on the reviewed literature, AI has significantly advanced IAM systems in industrial environments, providing sophisticated tools for security and efficiency. The continuous evolution of AI technologies promises to further enhance IAM capabilities, addressing current challenges and meeting future security needs.  
All authors contributed in all stages of the research and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no conflicts of interest.  
Summary of Reviewed Articles  
Summary of the application domain, objective, methodology, and strengths of the articles considered in this review.