Background: The integration of Artificial Intelligence (AI) into medical image processing has significantly enhanced diagnostic accuracy. AI-powered
Diagnostic Aid Systems (DAS) are transforming radiology, pathology, and other image-dependent specialties by automating detection, classification, and segmentation tasks. With increasing volumes of imaging data and a global shortage of expert radiologists,
AI offers a scalable and efficient solution for improving diagnostic precision and supporting clinical decision-making. Methods: This study systematically reviewed peer-reviewed literature published between ۲۰۲۱ and ۲۰۲۵ on the application of
AI in enhancing diagnosis accuracy in DAS for medical imaging. Various
AI techniques, including convolutional neural networks (CNNs), deep learning, and transformer-based architectures, were evaluated. Comparative performance metrics (sensitivity, specificity, accuracy, and AUC) of
AI models versus human experts and traditional rule-based systems were analyzed across modalities such as X-ray, MRI, CT, and digital pathology. Results: AI-enhanced DAS demonstrated superior accuracy compared to conventional systems across all studied modalities. For instance, deep learning models achieved a pooled sensitivity of ۹۶.۳% and specificity of ۹۳.۳% in digital pathology applications. In brain metastases detection using MRI,
AI algorithms showed a detectability rate of ۸۹%. Furthermore,
AI tools in diagnostic imaging have been shown to reduce errors and accelerate diagnostic processes, leading to quicker patient diagnosis and reduced healthcare costs. Discussion: The results affirm AI's potential in enhancing diagnostic accuracy, particularly in settings with limited medical resources or high workloads. However, challenges such as data bias, model interpretability, and clinical integration persist. Standardizing model evaluation and promoting transparent
AI systems are essential to gaining clinician trust and ensuring ethical deployment. Conclusion: AI-driven
Diagnostic Aid Systems significantly improve diagnostic accuracy in medical image processing and hold promise for global healthcare enhancement. Future research should focus on real-world deployment, interoperability, and regulatory frameworks to fully harness AI’s potential in clinical practice.
