The Pigeon-Inspired Optimization (PIO) algorithm constitutes a metaheuristic method derived from the homing behaviour of pigeons. Initially formulated for three-dimensional path planning in unmanned aerial vehicles (UAVs), the algorithm has attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation, coupled with advantages in real-time performance and robustness. Nevertheless, as applications have diversified, limitations in convergence precision and a tendency toward premature convergence have become increasingly evident, highlighting a need for improvement. This review systematically outlines the developmental trajectory of the PIO algorithm, with a particular focus on its core applications in UAV navigation, multi-objective formulations, and a spectrum of variant models that have emerged in recent years. It offers a structured analysis of the foundational principles underlying the PIO. It conducts a comparative assessment of various performance-enhanced versions, including hybrid models that integrate mechanisms from other optimization paradigms. Additionally, the strengths and weaknesses of distinct PIO variants are critically examined from multiple perspectives, including intrinsic algorithmic characteristics, suitability for specific application scenarios, objective function design, and the rigor of the statistical evaluation methodologies employed in empirical studies. Finally, this paper identifies principal challenges within current PIO research and proposes several prospective research directions. Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator. Enhancing parameter adaptation strategies, potentially using reinforcement learning for dynamic tuning, and advancing theoretical analyses on convergence and complexity are also critical. Further applications should be explored in constrained path planning, Neural Architecture Search (NAS), and other real-world multi-objective problems. For Multi-objective PIO (MPIO), key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency. These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.

The YOLO (You Only Look Once) series, a leading single-stage object detection framework, has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance. Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection, lesion segmentation, and microscopic image analysis, thereby accelerating the development of clinical decision support systems. This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024. It compares YOLO’s performance with other models (e.g., Faster R-CNN, RetinaNet) in medical contexts, summarizes standard evaluation metrics (e.g., mean Average Precision (mAP), sensitivity), and analyzes hardware deployment strategies using public datasets such as LUNA16, BraTS, and CheXpert. The review highlights the impressive performance of YOLO models, particularly from YOLOv5 to YOLOv8, in achieving high precision (up to 99.17%), sensitivity (up to 97.5%), and mAP exceeding 95% in tasks such as lung nodule, breast cancer, and polyp detection. These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications, indicating their ability to enhance clinical workflows. However, the study also identifies key challenges, including high small-object miss rates, limited generalization in low-contrast images, scarcity of annotated data, and model interpretability issues. Finally, the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.