A Systematic Literature Review of You Only Look Once Architectures (v1–v12) in Healthcare Systems

TL;DR

This paper reviews the development of YOLO object detection models from v1 to v12 and their use in healthcare for medical image analysis and diagnosis.

Contribution

A systematic review of YOLO architectures in healthcare, analyzing their performance and evolution for diagnostic applications.

Findings

YOLOv5 and YOLOv8 are most commonly used in healthcare due to their accuracy and efficiency.

YOLO models show strong performance in radiology, pathology, ophthalmology, and endoscopy.

Challenges remain in model interpretability and deployment on edge devices.

Abstract

Background/Objectives: The integration of deep learning and computer vision into healthcare has improved medical diagnosis and image analysis. Among object detection algorithms, the YOLO family has attracted substantial attention due to its ability to analyze images in real time with reported improvements in detection performance across multiple studies. This systematic review examines the evolution of YOLO algorithms for diagnostic applications in healthcare from YOLOv1 to YOLOv12. Methods: Peer-reviewed scientific articles published up to 1 January 2026 were retrieved from major scientific databases in accordance with PRISMA 2020 guidelines. The included studies applied YOLO models to medical imaging tasks, including disease and lesion detection and support for clinical procedures. Performance was synthesized using reported metrics such as average precision, accuracy, inference time, and computational efficiency. Results: The reviewed literature suggests progressive architectural refinements associated with reported improvements in diagnostic performance. YOLOv5 and YOLOv8 are the most frequently used architectures in diagnostic settings, reflecting a favorable trade-off between accuracy and computational complexity. YOLO-based methods have demonstrated strong performance across radiological, pathological, ophthalmological, and endoscopic applications. Conclusions: YOLO models have matured into robust and optimized solutions for medical image analysis; however, challenges remain in interpretability, cross-institution generalization, and deployment on edge devices. Future work on explainable YOLO-based diagnostics and energy-efficient model design will be particularly valuable.

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