TY  - JOUR
T1  - Multimodal Fusion Artificial Intelligence Models for Pathological Diagnosis in Early Cervical Cancer Screening: A Narrative Review 
AU  - Wei, Zhi-Feng
AU  - Qin, He
AU  - Lu, Shui-Juan
AU  - Ruan, Ping
AU  - Zhang, Ze-Chao
AU  - Zhu, Min
JF  - Oncology Advances
VL  - 4
IS  - 2
SN  - 2996-3427
SP  - e00004
EP  - e00004
Y1  - 2026-06-30
DO  - 10.14218/OnA.2026.00004
UR  - https://www.xiahepublishing.com/2996-3427/OnA-2026-00004
AB  - Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.