Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD’s applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD’s effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature.

We examine the transformative potential of artificial intelligence (AI) in medical imaging diagnosis, focusing on improving diagnostic accuracy and efficiency through advanced algorithms. It addresses the significant challenges preventing immediate clinical adoption of AI, specifically from technical, ethical, and legal perspectives. The aim is to highlight the current state of AI in medical imaging and outline the necessary steps to ensure safe, effective, and ethically sound clinical implementation. We conduct a comprehensive discussion, with special emphasis on the technical requirements for robust AI models, the ethical frameworks needed for responsible deployment, and the legal implications, including data privacy and regulatory compliance. Explainable artificial intelligence (XAI) is examined as a means to increase transparency and build trust among healthcare professionals and patients. The analysis reveals key challenges to AI integration in clinical settings, including the need for extensive high-quality datasets, model reliability, advanced infrastructure, and compliance with regulatory standards. The lack of explainability in AI outputs remains a barrier, with XAI identified as crucial for meeting transparency standards and enhancing trust among end users. Overcoming these barriers requires a collaborative, multidisciplinary approach to integrate AI into clinical practice responsibly. Addressing technical, ethical, and legal issues will support a softer transition, fostering a more accurate, efficient, and patient-centered healthcare system where AI augments traditional medical practices.