Transformers for 3D medical image analysis: a systematic review of architectural innovations, performance, and clinical applications

Abstract

The growing integration of Transformer-based architectures into 3D medical image analysis has driven significant advances across segmentation, classification, detection, registration, and reconstruction tasks. However, existing reviews remain fragmented, often focusing on 2D medical image analysis or specific modalities or tasks without providing a comprehensive, structured synthesis of architectural innovations, benchmark performance, and clinical applicability. This systematic review addresses these gaps by following the PRISMA 2020 framework to systematically evaluate 138 peer-reviewed studies published between January 2017 and December 2025, identified across Scopus, Web of Science, and PubMed. We evaluate Transformer-based and hybrid CNN–Transformer architectures across major 3D imaging modalities; MRI, CT, PET, and ultrasound using a structured five-question research framework addressing architectural evolution, benchmark performance, modality-specific trends, methodological rigour, and reproducibility. To quantify methodological progress beyond performance metrics, we introduce an Architectural Innovation Score grounded in a component-level innovation encoding scheme. Our analysis reveals a clear dominance of hybrid CNN–Transformer architectures, with MRI representing the most extensively studied modality. Multimodal imaging achieves the highest normalized mean performance, followed by MRI, CT, and ultrasound. Emerging paradigms, including State Space Models and diffusion-based Transformers, show promise but remain underexplored. Despite strong benchmark results, critical limitations persist, including insufficient external validation, limited code availability, and inconsistent reporting practices.