Optical Coherence Tomography and Artificial Intelligence in Gynecologic Diagnostics: A Comprehensive Review of Technological Advancements and Clinical Applications

Gynecologic malignancies present a persistent global health challenge that necessitates diagnostic modalities capable of transcending the limitations of conventional colposcopy-guided biopsy. Optical coherence tomography (OCT) has emerged as a transformative solution, offering non-invasive, micrometer-scale visualization of tissue micro-architecture. However, the inherent complexity of high-dimensional OCT data has hindered its clinical translation. Artificial intelligence (AI) is now poised to bridge this interpretability gap and evolve OCT from a qualitative imaging tool into a quantitative, automated diagnostic system. Addressing the lack of consolidated frameworks in this emerging field, this article presents the first comprehensive systematic review bridging gynecologic diagnostics, OCT technology, and AI. We synthesize the field from 2000 to 2025 by integrating evidence from a wide-ranging survey of the literature that encompasses a large cohort of clinical studies and patient data across diverse international settings. Moving beyond a conventional enumeration of studies, we unify scattered methodologies into a cohesive theoretical paradigm organized across three synergistic layers: Foundational Technologies, detailing advances in hardware acceleration and signal reconstruction; AI-Empowered Diagnosis, covering deep learning paradigms for lesion segmentation, classification, and multi-modal fusion; and Clinical Translation, examining the engineering pathways toward real-world deployment. Furthermore, we critically analyze persistent barriers, including biomarker inconsistency, limited model generalizability, and regulatory challenges, and outline a strategic roadmap to accelerate the integration of trustworthy AI-driven OCT into precision gynecologic care, especially in low-resource settings. This survey provides essential context and a technical synthesis to advance the next generation of intelligent gynecologic diagnostics based on OCT and AI.