When Many Minds Focus: A Review of Attention-Enhanced Ensemble Models in Medical Imaging

The increasing complexity of medical data presents persistent challenges for traditional analytical methods. Conventional machine learning models often exhibit practical limitations in clinical settings, including a susceptibility to overfitting and a lack of transparency that can impede their adoption. This systematic review investigates the integration of attention mechanisms with ensemble learning, an approach this paper formalizes as Attention-Enhanced Ensemble Models (AEEM), to serve as a direct response to these issues. Conducted according to PRISMA guidelines, the review analyzes 19 primary studies to evaluate the effectiveness of this combined approach in medical imaging. The analysis reveals that AEEMs demonstrate consistent performance improvements, with gains ranging from 1.05% to 29.5% over baseline models, alongside enhanced model interpretability and robustness. The systematic examination also identifies common patterns in component selection and highlights significant gaps in current reproducibility practices. The synthesis of these empirical findings informs a systematic decision framework designed to provide evidence-based guidance for the implementation of AEEMs.