Artificial Intelligence in Gynaecology: A Narrative Review of Diagnostic, Surgical, and Educational Applications

Background Artificial Intelligence (AI) is rapidly evolving and is increasingly applied across healthcare disciplines. In gynaecology, however, its implementation is still emerging and not yet well established. This review explores how AI is being applied in gynaecology within the domains of diagnostics, surgery, and education. It aims to map current research, identify key technologies, and highlight potential benefits and challenges. Methods A literature search was conducted using the PubMed database for the last ten years (2014–2025), using a targeted AI and gynaecology search strategy. Studies were screened based on inclusion criteria, and 11 eligible articles were selected. Data were charted based on study design, AI method, clinical domain, key outcomes, and limitations. Results Eleven studies were included: 4 focused on diagnostics, 3 on surgery, and 4 on education. AI was applied for cancer screening, embryo assessment, robotic-assisted surgery, surgical workflow optimization, and educational simulations. AI models included neural networks, machine learning algorithms, and vision-based tools. Benefits included improved diagnostic accuracy, reduced surgical complications, and enhanced training outcomes. Conclusion AI shows promise in advancing diagnostic precision, supporting safer and more effective surgical interventions, and enhancing medical education in gynaecology. However, challenges such as ethical concerns, data privacy, interpretability, and lack of clinical validation remain. Continued multidisciplinary research and responsible integration are needed to fully realize AI’s potential in gynaecology.