Artificial intelligence (AI) is transforming occupational health and safety (OHS) by enabling advanced technologies to detect occupational lung diseases and enhance workplace safety. The integration of AI-driven solutions, such as machine learning, neural networks, and computer vision, is revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies. These technologies help minimize accidents and hazards, promoting a more proactive and responsive approach to safeguarding the workforce. AI algorithms, particularly those based on deep learning (DL), have shown great promise in lung image processing, significantly improving disease diagnosis based on plain radiographs. For example, AI can analyze chest X-rays (CXRs), computed tomography (CT) scans, or magnetic resonance imaging (MRI) scans to detect and classify abnormalities, identify nodules, masses, or patterns indicative of diseases like mesothelioma, COPD, and silicosis. AI models can also help in data augmentation, mitigation of image noise, and synthetic data generation, which can generate synthetic lung images that resemble real patient data. In the context of pneumoconiosis, AI has been used to address various aspects of occupational health and hazards, primarily in enhancing diagnostic precision and accuracy. Neural networks (NNs) have been applied to remove background noise in CXRs, improving the detection of small rounded opacities. Additionally, DL methods have revolutionized non-texture CXR analysis by offering automated disease detection, segmentation, localization, and interpretability, significantly improving the accuracy, efficiency, and utility of pneumoconiosis classification and interpretation in clinical practice. AI-based solutions, such as vision transformers (ViT), have demonstrated improved capability in predicting the initial phase of pneumoconiosis, with high accuracy and F1 scores. AI also plays a crucial role in enhancing workplace safety through various technologies, including AI-driven exoskeletons, smart PPE, and AI-based robots. These technologies help protect worker well-being, reduce injuries, and improve overall workplace conditions. AI computer vision is also being used in monitoring and surveillance tools for workplace safety, enabling real-time alerts and risk identification. Virtual reality (VR) is being used for safety training, providing practical knowledge and experience in risky situations. AI-driven site drones are being used for pre-construction site inspections, maintenance inspections, and other tasks, enhancing safety and efficiency in construction projects. Overall, AI is playing a pivotal role in advancing occupational health and safety, offering innovative solutions to detect occupational diseases, enhance workplace safety, and improve overall worker well-being.Artificial intelligence (AI) is transforming occupational health and safety (OHS) by enabling advanced technologies to detect occupational lung diseases and enhance workplace safety. The integration of AI-driven solutions, such as machine learning, neural networks, and computer vision, is revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies. These technologies help minimize accidents and hazards, promoting a more proactive and responsive approach to safeguarding the workforce. AI algorithms, particularly those based on deep learning (DL), have shown great promise in lung image processing, significantly improving disease diagnosis based on plain radiographs. For example, AI can analyze chest X-rays (CXRs), computed tomography (CT) scans, or magnetic resonance imaging (MRI) scans to detect and classify abnormalities, identify nodules, masses, or patterns indicative of diseases like mesothelioma, COPD, and silicosis. AI models can also help in data augmentation, mitigation of image noise, and synthetic data generation, which can generate synthetic lung images that resemble real patient data. In the context of pneumoconiosis, AI has been used to address various aspects of occupational health and hazards, primarily in enhancing diagnostic precision and accuracy. Neural networks (NNs) have been applied to remove background noise in CXRs, improving the detection of small rounded opacities. Additionally, DL methods have revolutionized non-texture CXR analysis by offering automated disease detection, segmentation, localization, and interpretability, significantly improving the accuracy, efficiency, and utility of pneumoconiosis classification and interpretation in clinical practice. AI-based solutions, such as vision transformers (ViT), have demonstrated improved capability in predicting the initial phase of pneumoconiosis, with high accuracy and F1 scores. AI also plays a crucial role in enhancing workplace safety through various technologies, including AI-driven exoskeletons, smart PPE, and AI-based robots. These technologies help protect worker well-being, reduce injuries, and improve overall workplace conditions. AI computer vision is also being used in monitoring and surveillance tools for workplace safety, enabling real-time alerts and risk identification. Virtual reality (VR) is being used for safety training, providing practical knowledge and experience in risky situations. AI-driven site drones are being used for pre-construction site inspections, maintenance inspections, and other tasks, enhancing safety and efficiency in construction projects. Overall, AI is playing a pivotal role in advancing occupational health and safety, offering innovative solutions to detect occupational diseases, enhance workplace safety, and improve overall worker well-being.