Yousef Farhaoui is a professor of Computer Science at the Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University, Morocco, and the Director of the Laboratory of Computer Science, Mathematics, and Artificial Intelligence (IMIA). He is internationally recognized and listed among the world’s top 2% most influential scientists according to the Stanford & Elsevier rankings (2023–2025). He leads multiple academic programs, including coordination of the Master’s program in Decision Support Systems and Imaging (SIDI) and the Computer Engineering cycle, while also directing the IMIA research laboratory and supervising doctoral research in artificial intelligence, decision support systems, and smart computing. His research activities are strongly centered on AI, big data, smart environments, decision systems, and applied machine learning. He has an extensive record of academic leadership and scientific event organization, having coordinated numerous international conferences and workshops on big data, artificial intelligence, and digital environments across Europe, Asia, and North America. He is also the main organizer of the International Conference on Artificial Intelligence and Smart Environment (ICAISE). In addition to his research and teaching, he has supervised multiple PhD theses, serves on academic recruitment and evaluation committees, and contributes to international scientific associations. His work focuses on advancing intelligent systems, data-driven decision-making, and AI applications for smart environments and digital transformation.

Explainable and Sustainable Artificial Intelligence for Smart Healthcare Systems Artificial Intelligence (AI) is transforming modern healthcare by enabling intelligent diagnosis, predictive analytics, personalized treatment, and smart medical decision support systems. However, the increasing complexity of deep learning models raises significant challenges related to explainability, transparency, data privacy, and sustainability. This work explores the integration of Explainable Artificial Intelligence (XAI) and sustainable AI techniques in smart healthcare environments to improve reliability, interpretability, and energy-efficient medical intelligence. The proposed framework combines machine learning, deep neural networks, and explainable models to support early disease detection and clinical decision-making while ensuring transparency and trustworthiness. Particular attention is given to healthcare data analytics, medical imaging, IoT-enabled healthcare systems, and predictive monitoring applications. Furthermore, the study investigates optimization strategies for reducing computational complexity and energy consumption in AI-driven healthcare infrastructures. Experimental results demonstrate that integrating explainability mechanisms with intelligent healthcare models significantly improves diagnostic confidence and system usability for healthcare professionals. The proposed approach contributes to the development of secure, transparent, and sustainable AI-powered healthcare ecosystems aligned with future smart medicine and digital health initiatives. Keywords: Artificial Intelligence, Explainable AI, Smart Healthcare, Machine Learning, Deep Learning, Sustainable AI, Predictive Analytics, Medical Data Science, IoT Healthcare, Intelligent Decision Support Systems.