Review Article
Artificial Intelligence in Radiology: A Survey on Transforming Diagnostic Accuracy and Clinical
Decision-Making
Issue:
Volume 1, Issue 2, June 2026
Pages:
49-56
Received:
11 January 2026
Accepted:
21 January 2026
Published:
15 April 2026
DOI:
10.11648/j.sdh.20260102.11
Downloads:
Views:
Abstract: Artificial Intelligence (AI) has emerged as a transformative force in modern radiology, driven by rapid advances in machine learning (ML) and deep learning (DL) techniques. As radiology is a data-intensive specialty, the increasing volume and complexity of medical imaging have created a growing demand for intelligent tools that can enhance diagnostic accuracy, efficiency, and clinical decision-making. This survey-based review aims to evaluate and assess the current role of AI in radiology and its impact on diagnostic performance and clinical practice. This study systematically reviews and synthesizes peer-reviewed literature published between 2019 and 2025, focusing on AI applications across major imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-ray, mammography, ultrasound, and positron emission tomography (PET). Relevant studies were identified through major academic databases, and the findings were analyzed narratively to assess improvements in diagnostic accuracy, workflow optimization, and decision-support capabilities. Particular attention was given to commonly used AI algorithms, such as convolutional neural networks (CNNs), ResNet, DenseNet, transformer-based models, and radiomics-driven machine learning approaches. The reviewed evidence demonstrates that AI-assisted radiology systems consistently achieve high levels of diagnostic accuracy, sensitivity, and specificity, in many cases comparable to or exceeding those of expert radiologists. AI tools also contribute to reduced reporting times, improved interobserver consistency, and enhanced prioritization of urgent cases. Furthermore, the integration of AI into clinical decision support systems enables predictive analytics that support personalized treatment planning and disease monitoring. Despite these benefits, this survey highlights several challenges that limit widespread clinical adoption, including data heterogeneity, limited external validation, algorithmic bias, lack of transparency, and evolving ethical and regulatory frameworks. In conclusion, AI represents a powerful complementary tool that enhances, rather than replaces, the role of radiologists. Continued interdisciplinary collaboration, rigorous validation, and responsible governance are essential to ensure the safe and effective integration of AI into radiological practice.
Abstract: Artificial Intelligence (AI) has emerged as a transformative force in modern radiology, driven by rapid advances in machine learning (ML) and deep learning (DL) techniques. As radiology is a data-intensive specialty, the increasing volume and complexity of medical imaging have created a growing demand for intelligent tools that can enhance diagnost...
Show More
Research Article
Prevalence and Impact of Postmastectomy Pain Syndrome Among Breast Cancer Survivors at the Tumor Therapy and Cancer Research Center, Shendi, Sudan
Issue:
Volume 1, Issue 2, June 2026
Pages:
57-66
Received:
28 March 2026
Accepted:
16 April 2026
Published:
29 April 2026
DOI:
10.11648/j.sdh.20260102.12
Downloads:
Views:
Abstract: Introduction: Postmastectomy Pain Syndrome (PMPS) is a chronic neuropathic pain condition affecting a significant proportion of breast cancer (BC) survivors, compromising their quality of life (QoL). Despite its clinical significance, PMPS remains underreported and inadequately managed, particularly in resource-limited settings such as Sudan. This study aimed to assess the prevalence, severity, and QoL impact of PMPS among breast cancer survivors in Sudan, while exploring risk factors and current gaps in pain management. Methods: A cross-sectional study was conducted among 116 postmastectomy patients at the Tumor Therapy and Cancer Research Center, Shendi. Data were collected through structured interviews and questionnaires, evaluating demographic characteristics, pain characteristics (prevalence, severity), and QoL. Associations between PMPS and clinical variables (surgery type, radiotherapy (RT), chemotherapy) were analyzed using statistical tests. Results: The prevalence of PMPS was 35.3%. Pain was predominantly mild to moderate but significantly impaired physical functioning and social participation. PMPS was associated with the type of surgery (P = 0.021), radiotherapy (RT) (P = 0.024), and neoadjuvant chemotherapy (NACT) (P = 0.016). Notably, there was a low utilization of pain management strategies, with only 21.9% of participants used analgesic medications, and 85.4% reported no preoperative counselling about PMPS. Conclusion: PMPS is highly prevalent and debilitating among breast cancer survivors in Sudan, marked by significant gaps in pain management and patient education, which substantially impair their quality of life. These findings underscore the need for standardized pain management protocols, routine preoperative counselling, and multidisciplinary support services to improve PMPS care. Future research should explore context-specific interventions to reduce the burden of PMPS and enhance the long-term well-being of breast cancer survivors."
Abstract: Introduction: Postmastectomy Pain Syndrome (PMPS) is a chronic neuropathic pain condition affecting a significant proportion of breast cancer (BC) survivors, compromising their quality of life (QoL). Despite its clinical significance, PMPS remains underreported and inadequately managed, particularly in resource-limited settings such as Sudan. This ...
Show More
,
Maysoon Saud Saleh Alharthy
