Muhammad Hammad u Salam’s scientific contributions

The use of Artificial Intelligence, especially machine learning and artificial neural networks, has dramatically increased in urology, assisting in innovative ways for diagnosis, prognosis, and treatment planning. This paper presents an up-to-date review of AI advances in the field of urology that pertain to its imaging aspects, particularly regarding the diagnosis of prostate cancer, kidney stones, and bladder cancer. The deep learning methods, especially convolutional neural networks, proved to be very effective in many medical imaging tasks, such as automated abnormal growth detection, organ segmentation, etc. Additionally, deep learning systems have performed well in predicting a patient’s outcome, including post-operative complications and recovery. Nevertheless, the progress made greatly differs from the goals set, and AI’s integration into clinical practice remains an unmet need due to obstacles posed by inefficient datasets and the opacity of some AI algorithms.This paper also discusses the key challenges in implementing AI tools in urology, as well as the potential for future research to enhance the accuracy, interpretability, and clinical applicability of AI-driven solutions. Ultimately, AI is poised to play a transformative role in urology, offering the potential for more personalized, efficient, and precise patient care.

This paper analyzes the transformation that immersion technologies and machine learning (ML) invoke on modern management, particularly regarding its application in the core functions of an organization including recruitment, employee training, talent management, motivation, and corporate culture build up. In recruitment alone, ML algorithms facilitate organizational decision-making by predicting candidate suitability, behavior and optimizing hiring processes. Furthermore, immersion technologies like virtual reality (VR) can be employed to solve training and development problems by providing high-quality learning opportunities for active engagement and improvement of staff’s productivity and effectiveness. These advancements, however, pose new risks such as privacy, ethics, and data discrimination. This paper covers the opportunities and challenges provided by ML and immersive technologies in management with an emphasis on its utilization in the organization performance and satisfaction of employees. In summary, while the benefits of adopting these technologies with regard to improving efficiency as well as effectiveness of business operations is evident, the policies governing adoption and utilization will require a great deal of retrieval on the ethical borders and implications of such actions.