Artificial Intelligence in Diagnostic Imaging and Digital Pathology: Redefining Accuracy, Efficiency, and Early Disease Detection
Keywords:
Artificial Intelligence, Diagnostic Imaging, Radiology, Digital Pathology, Deep Learning, Medical Image Analysis, Precision DiagnosticsAbstract
Diagnostic imaging and pathology form the backbone of modern clinical decision-making, yet they face increasing pressure from growing data volumes, workforce shortages, and the demand for faster and more accurate diagnoses. Artificial Intelligence (AI), particularly deep learning, has emerged as a transformative technology capable of addressing these challenges. In radiology, AI systems now demonstrate expert-level performance in image interpretation across modalities such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and mammography. In parallel, digital pathology has been revolutionized by machine learning models that analyze whole-slide images to detect cancer, grade tumors, and identify prognostic biomarkers with high consistency. This editorial examines recent advances in AI-driven diagnostic imaging and digital pathology, highlighting their clinical impact, integration into workflows, and potential to enable earlier detection, reduce diagnostic variability, and support precision medicine. The discussion also addresses challenges related to data quality, interpretability, and ethical deployment, emphasizing the need for responsible and clinician-centered AI adoption in healthcare.
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Copyright (c) 2026 Chiung Min Lee (Author)
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