Radiology report generation is a critical task in medical imaging because it allows for rapid and accurate communication of diagnostic findings to referring physicians. There has been an increasing interest in using artificial intelligence methods to automate this process. The study presents a practical exploration of radiology report simulations to improve diagnostic efficiency and accuracy, alongside a theoretical investigation into the integration of social robots in clinical settings. This dual approach highlights their potential to enhance social learning, foster collaboration in medical training, and improve patient wellness. The study adopted a deep learning-based model trained on a dataset of 7,470 chest X-ray images and their corresponding textual reports, with the potential for integration into social robots and AI-driven clinical assistants. The model architecture includes an encoder-decoder framework with an embedding size of 5,000, 256 encoder units, and 512 decoder units. The generated reports are cross verified with the actual reports to ensure clinical accuracy, enabling social robots to provide reliable diagnostic insights and enhance patient wellness through personal interactions. The model achieved a BLEU-4 score of 0.751, demonstrating its ability to create accurate and clinically relevant radiology reports from chest X-ray images, with beam search further enhancing performance. The results demonstrate that the system can produce accurate and clinically relevant reports with high fidelity. This work bridges the gap between automated report generation and clinical radiological analysis, offering the potential to integrate automated radiology report generation systems into social robots or AI-driven clinical assistants to enhance healthcare delivery and patient wellness.