AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction
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),...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/5/141 |
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| author | Abdussalam Elhanashi Sergio Saponara Qinghe Zheng Nawal Almutairi Yashbir Singh Shiba Kuanar Farzana Ali Orhan Unal Shahriar Faghani |
| author_facet | Abdussalam Elhanashi Sergio Saponara Qinghe Zheng Nawal Almutairi Yashbir Singh Shiba Kuanar Farzana Ali Orhan Unal Shahriar Faghani |
| author_sort | Abdussalam Elhanashi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-076ea6c4304342e48d2ffddaebc60e50 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-076ea6c4304342e48d2ffddaebc60e502025-08-20T02:33:57ZengMDPI AGJournal of Imaging2313-433X2025-04-0111514110.3390/jimaging11050141AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future DirectionAbdussalam Elhanashi0Sergio Saponara1Qinghe Zheng2Nawal Almutairi3Yashbir Singh4Shiba Kuanar5Farzana Ali6Orhan Unal7Shahriar Faghani8Department of Information Engineering, University of Pisa, 56122 Pisa, ItalyDepartment of Information Engineering, University of Pisa, 56122 Pisa, ItalySchool of Intelligence Engineering, Shandong Management University, Jinan 250100, ChinaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 145111, Saudi ArabiaDepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USADepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USADepartment of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USADepartments of Radiology, School of Medicine, Medical Physics University of Wisconsin-Madison, Public Health Madison, Madison, WI 53705, USADepartment of Radiology, Mayo Clinic, Rochester, MN 55905, USAArtificial 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.https://www.mdpi.com/2313-433X/11/5/141object detectionradiologyartificial intelligenceconvolutional neural networkdiagnostic accuracy |
| spellingShingle | Abdussalam Elhanashi Sergio Saponara Qinghe Zheng Nawal Almutairi Yashbir Singh Shiba Kuanar Farzana Ali Orhan Unal Shahriar Faghani AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction Journal of Imaging object detection radiology artificial intelligence convolutional neural network diagnostic accuracy |
| title | AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction |
| title_full | AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction |
| title_fullStr | AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction |
| title_full_unstemmed | AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction |
| title_short | AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction |
| title_sort | ai powered object detection in radiology current models challenges and future direction |
| topic | object detection radiology artificial intelligence convolutional neural network diagnostic accuracy |
| url | https://www.mdpi.com/2313-433X/11/5/141 |
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