Artificial Intelligence in Gynecological Oncology
Özet
Gynecologic cancers, including cervical, endometrial, and ovarian malignancies, remain significant causes of morbidity and mortality in women. Early diagnosis and accurate staging are vital to improving outcomes. Imaging modalities such as Ultrasonography, Magnetic Resonance İmaging and Computed Tomography play key roles in this process, and the integration of artificial intelligence (AI) has further revolutionized gynecologic oncology. AI, encompassing machine learning and deep learning, enables automated analysis of complex medical data, enhancing diagnostic accuracy, prognostic prediction, and treatment planning. Radiomics and radiogenomics bridge imaging with molecular data, supporting personalized medicine through non-invasive tumor characterization. In gynecologic oncology in particular, applications include differentiation between benign and malignant ovarian masses, prediction of lymph node metastasis in endometrial cancer, and prediction of parametrial invasion in cervical cancer. Despite promising advances, challenges remain regarding data heterogeneity, standardization, ethical use, and model explainability. Future AI systems integrating clinical, imaging, genomic, and metabolomic data could enable real-time, individualized decision support. Implemented under robust regulatory frameworks, AI has the potential to significantly improve diagnostic precision, treatment efficacy, and overall survival in gynecologic oncology.
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