The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care in the next generation of diagnostic imaging.
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in diagnostic radiology, offering innovative solutions for image interpretation, workflow optimization, and clinical decision-making. Recent advances in machine learning, deep learning, and related computational approaches have enabled the development of systems capable of analyzing complex imaging datasets with high accuracy and efficiency. AI applications are increasingly being integrated across multiple imaging modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, mammography, and positron emission tomography. In addition to disease detection and classification, AI supports image segmentation, radiomics, predictive analytics, image reconstruction, automated reporting, and quality assurance, thereby enhancing diagnostic performance and operational efficiency. Despite these advances, challenges related to data quality, algorithmic bias, generalizability, explainability, ethical considerations, and regulatory compliance continue to influence the clinical adoption of AI technologies. Emerging developments such as foundation models, generative artificial intelligence, large language models, multimodal learning, and privacy-preserving techniques are expected to further expand the role of AI in medical imaging. This review provides an overview of the fundamental concepts of AI in radiology, examines its current clinical applications, discusses key challenges and limitations, and explores future directions that may shape the next generation of diagnostic imaging. The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care.
Ultrasound imaging is widely used across cardiology, hepatology, obstetrics, breast and thyroid imaging, and emergency care because it is real-time, noninvasive, and relatively accessible. However, its diagnostic performance remains influenced by operator experience, image quality, scanner settings, and interpretation variability. Artificial intelligence (AI) has emerged as a promising support tool for ultrasound, assisting with image acquisition, quality assessment, view classification, segmentation, measurement, lesion characterization, and structured reporting. This narrative review summarizes recent developments in AI-assisted ultrasound imaging, emphasizing its technical foundations, clinical applications, validation challenges, and relevance to Gulf health care systems. Current evidence suggests that AI may improve workflow efficiency, reduce interobserver variability, and support diagnostic decision-making in selected ultrasound tasks, particularly when models are trained and tested on large, diverse data sets. Nevertheless, the clinical readiness of many AI tools remains constrained by retrospective study designs, single-center data sets, limited external validation, vendor-dependent image variability, and insufficient prospective evaluation. These limitations are particularly salient in Gulf health care, where ultrasound services are delivered across heterogeneous public, private, military, and academic institutions that use different equipment, workflows, and operator training backgrounds. Gulf countries are well-positioned to adopt AI-enabled ultrasound because of the ongoing digital health transformation and emerging regulatory frameworks, including the Saudi SFDA guidance and the UAE AI governance initiatives. However, responsible implementation will require region-specific, multicenter, multivendor validation, transparent reporting of model performance and failure cases, clinician training, and privacy-preserving data governance. AI should therefore be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.
Bayan Alghamdi, Eman M Alrewily, Sharefa S Alghamdi· Ultrasound Quarterly· 0 citations
Artificial intelligence (AI) and deep learning (DL) technologies have revolutionized medical imaging and diagnostics. This comprehensive review synthesizes current evidence on the application, diagnostic accuracy, challenges, and future directions of AI algorithms in ultrasound (US) imaging. We conducted a systematic analysis of peer-reviewed literature examining diagnostic accuracy of deep learning in medical imaging and specific AI applications in ultrasound-guided procedures, breast US diagnosis, AI-based radiomics, and regional anesthesia guidance. Our findings demonstrate that AI algorithms achieve high diagnostic accuracy across multiple ultrasound applications, with sensitivity and specificity often comparable to or exceeding experienced radiologists. In ophthalmology imaging, AI achieved area under curve (AUC) of 0.939-0.969 for various retinal pathologies. In breast ultrasound, AI systems demonstrated sensitivity of 92.5% and accuracy of 78.6% for malignant lesion detection. Deep learning radiomics achieved AUC of 0.978 in pancreatic adenocarcinoma diagnosis and 0.97 in breast cancer characterization. However, significant challenges remain including standardization of training datasets, external validation, clinical workflow integration, regulatory compliance, and reimbursement issues. This review highlights that while AI shows tremendous promise for enhancing diagnostic accuracy and clinical efficiency in ultrasound medicine, successful implementation requires multidisciplinary collaboration, robust validation frameworks, and organizational infrastructure for sustainable clinical integration.
Thyroid nodules are highly prevalent, with increasing detection rates driven by advanced imaging and expanded screening. Ultrasound serves as the first-line tool for screening, diagnosis and follow-up, owing to its non-invasiveness, real-time capability, cost-effectiveness and absence of ionizing radiation. However, conventional ultrasound diagnosis is highly operator-dependent, resulting in substantial inter-observer variability and diagnostic errors, particularly for subtle or indeterminate lesions. Artificial intelligence (AI), particularly deep learning and radiomics, has emerged as a promising approach to address these limitations by enabling automated feature extraction, quantitative analysis and standardized interpretation, which has the potential to improve diagnostic efficiency and risk stratification. This review summarizes AI applications in thyroid ultrasound, including image preprocessing, nodule segmentation, quantitative feature analysis, benign-malignant differentiation, TIRADS optimization and automated reporting. We highlight AI’s potential in enhancing diagnostic consistency and accuracy, while critically assessing the methodological quality, bias risks and external validation of existing studies. Most AI tools are still in early translational phases, lacking large-scale validation in real clinical settings and standardized reporting protocols. We further discuss key challenges, including data bias, limited generalizability due to small or single-center datasets, poor interpretability and significant translational barriers. Future directions involving multi-modal fusion, explainable AI, real-time clinical systems and rigorous, multi-center standardized validation are proposed to facilitate clinical translation and improve patient care.
Ye Guo, Tong Zhao, Li-li Zhang et al.· Frontiers in Endocrinology· 0 citations
The integration of artificial intelligence (AI) into digital pathology is perhaps the most revolutionary leap forward in modern diagnostic medicine. The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems. AI systems have achieved pathologist-level performance in controlled settings, including diagnostic accuracy >99% and area under the receiver operating characteristic curve values exceeding 0.97. However, translating research into clinical adoption is riddled with several challenges attributable to computational requirements, data standardization issues, regulatory hurdles and limitations in generalizability. Moreover, Vision Transformers are widely popular as powerful alternatives to conventional convolutional models, delivering high performance in certain domains while also imposing a novel computational burden. Overcoming these challenges is a prerequisite for the successful integration of AI into pathology practice and the realization of its full diagnostic potential.
Abdul-Mohsen G. Alhejaily, D. Alghamdi· Biomedical Reports· 0 citations
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Lakshmi Sai Anusha Dadi, Pravallika Devi Kommana· International Journal for Re...· 0 citations
Breast cancer (BC) continues to be the most prevalent malignancy affecting women globally, representing a major public health concern, with significant morbidity and mortality. Early detection, accurate diagnosis, and precise characterisation of breast lesions are important to improve patient outcomes and survival rates. Conventional imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI), have played pivotal roles in BC diagnosis but face limitations related to subjective interpretation, variability between radiologists, and challenges in detecting biologically aggressive subtypes. Radiomics and artificial intelligence (AI) have emerged as revolutionary adjuncts to enhance the diagnostic and prognostic capabilities of breast imaging. Radiomics involves the extraction of high-dimensional quantitative imaging features from standard medical images that are imperceptible to the human eye. These features can reveal tumour heterogeneity, microenvironment characteristics, and biological behaviour, thereby enriching information traditionally derived from visual inspection. AI, particularly through machine learning and deep learning models, enables automated analysis, pattern recognition, and prediction of clinical outcomes with high accuracy and reproducibility. The integration of radiomics and AI into BC imaging workflows holds the potential to shift the paradigm towards precision oncology, offering individualised risk stratification, early prediction of treatment response, and real-time decision support. However, this field faces significant challenges, including issues related to data standardisation, reproducibility, model validation, regulatory approval, and clinical integration. Ethical considerations regarding the data privacy, bias, and explainability of AI algorithms also remain critical hurdles. This comprehensive review delves into the fundamental concepts of radiomics and AI, summarises their current applications in BC imaging, and explores their evolving roles in clinical practice. It highlights recent advances, presents case studies demonstrating the clinical impact, and discusses ongoing research efforts aimed at overcoming the existing limitations. Furthermore, future directions, including the integration of radio genomics, explainable AI (XAI), and multi-omics approaches, were thoroughly examined to provide a roadmap for the clinical applicability of these technologies. As the convergence of advanced imaging analytics and computational intelligence continues to mature, radiomics and AI have been poised to redefine BC management, ushering in a new era of more accurate, efficient, and personalised patient care.
Priyanka Dutta· Karnataka Journal of Surgery· 0 citations