Abstract :
Clinical anatomy has conventionally depended on cadaveric dissection as the gold standard in understanding structural relationships in surgical practice. However, with the advent of radiomics, artificial intelligence (AI), and three-dimensional imaging techniques, anatomical understanding has transformed from a static science to a dynamic one. AI-assisted radiologic analysis now allows the quantitative evaluation of radiomic features that have been shown to relate to microanatomy, vascular variability, facial planes, and neural networks. The combination of radiomic analysis with cadaveric correlation is proposed as an emerging framework in precision surgery and personalized anatomical understanding. In this review, we explore the interface between AI, radiomics, and clinical anatomy from an evolving perspective with specific emphasis on their potential applications in neurosurgery, hepatobiliary surgery, vascular interventions, and minimally invasive surgical procedures. By fusing radiologic intelligence with traditional anatomical understanding, AI-enhanced clinical anatomy promises to revolutionize surgical practice in the future.
Keyword :
Artificial intelligence, Radiomics, Clinical anatomy, Cadaveric correlation, Precision surgery, 3D reconstruction, Surgical navigation, Anatomical variation, Personalized medicine, Machine learning