A Systematic Review of YOLO-Based Object Detection in Medical Imaging: Advances, Challenges, and Future Directions

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.