Artificial intelligence and modern surgical energy-based technologies in the treatment of women of reproductive age with adenomyosis and uterine fibroids (literature review and own data)

Benign uterine diseases remain among the leading causes of surgical interventions in women of reproductive age. A forty-year cumulative clinical experience (1986—2026) demonstrates that the effectiveness of reconstructive and uterus-preserving surgery for adenomyosis, uterine fibroids, and combined forms of pathology is determined not only by the radical removal of pathological lesions, but primarily by the accuracy of defining the extent of surgery and by preservation of the functional integrity of the residual myometrium. At all stages in the evolution of surgical approaches—from early methods of quantitative assessment of lesion extent to modern navigation-assisted procedures—the principle of precision has remained the key condition for achieving a balance between surgical radicality, procedural safety, and preservation of the patient’s reproductive potential. The integration of artificial intelligence (AI), three-dimensional modeling, and intraoperative navigation does not replace the surgeon’s clinical judgment but rather represents its technological extension, enabling more objective delineation of pathological boundaries, optimization of resection volume, and improvement of the reconstructive stage, taking into account the functional characteristics of the residual myometrium and endometrium. Modern operative gynecology is focused not only on eliminating disease but also on maximizing the anatomical and functional preservation of the uterus, particularly in women who have not yet realized their reproductive plans. In this context, uterus-preserving approaches based on the integration of AI technologies and high-precision surgical energy modalities are becoming especially relevant for achieving highly precise interventions. This paper systematizes current evidence on the application of AI in diagnostics, preoperative modeling, and intraoperative navigation of surgical interventions for uterine fibroids and adenomyosis. The capabilities of machine learning and radiomics algorithms in the analysis of ultrasound and magnetic resonance images, automated lesion segmentation, three-dimensional uterine modeling, and personalization of surgical strategy are reviewed. AI support contributes to increased preoperative diagnostic accuracy and optimization of the extent of uterus-preserving surgery based on three-dimensional imaging, with the goal of maximizing preservation of the anatomical and functional integrity of the residual myometrium and endometrium. A separate section is devoted to novel surgical energy-based technologies, including radiofrequency and microwave ablation, as well as high-intensity focused ultrasound (HIFU). Their biophysical mechanisms, clinical effectiveness, safety, and early reproductive outcome data are discussed. The combination of image-guided technologies with AI algorithms is emphasized as a foundation for the transition to precision surgery. Thus, the integration of artificial intelligence and high-precision energy-based methods represents a promising direction in the development of operative gynecology, aimed at optimizing uterus-preserving interventions, reducing surgical trauma, and improving functional and reproductive outcomes in patients with benign uterine diseases.