AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction

Abstract

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.

Keywords: artificial intelligence; convolutional neural network; diagnostic accuracy; object detection; radiology.

Figure 1

Overview of the enhancements in the medical imaging pipeline due to AI implementations: early detection of diseases through deep learning models (CNN), decreased time taken for diagnosis, higher accuracy of images, wider availability of diagnostics across the clinical field, as well as more rapid and cost-effective treatment. AI, on the other hand, also optimizes the radiology processes, improves the accuracy of interpretation, and widens accessibility to the diagnostic tool, particularly in rural regions.

Figure 2

A comparison of different radiology techniques: X-ray imaging, PET (Positron Emission Tomography), CT (Computed Tomography), Ultrasound (US), and MRI (Magnetic Resonance Imaging).

Figure 3

Overview of various image preprocessing techniques used in AI-powered object detection in radiology. The diagram categorizes preprocessing methods into key areas, including spatial resampling, intensity normalization, image registration, data augmentation, noise reduction, and segmentation. Each category further details specific techniques such as resizing, histogram equalization, deformable alignment, flipping, Gaussian filtering, and region-based segmentation, highlighting their roles in enhancing image quality and improving model performance.

Figure 4

A sample of a CNN architecture designed for a brain MR image. The diagram illustrates the input layer (medical image), followed by convolutional layers, pooling layers, fully connected layers, and the final output layer [14].