A Review on Deep Learning and Hybrid Generative–Transformer Models for Brain Age Estimation with Focus on Recent Trends, Challenges, and Future Directions

Brain age estimation from magnetic resonance imaging (MRI) is a promising biomarker of cognitive health, accelerated ageing, and neurodegenerative risk. However, its clinical translation remains constrained by scanner heterogeneity, demographic imbalance, and challenges in jointly modelling local and global brain structure. This review synthesizes 52 studies identified through a PRISMA-informed process involving dual independent screening (Cohen’s κ = 0.94 for title and abstract screening and 0.90 for full-text screening). Convolutional neural networks (CNNs), generative models, Transformers, and hybrid approaches are compared descriptively, and eleven recurring findings are ranked by evidence strength. Hybrid local–global models report the lowest median internal mean absolute error (MAE) of 2.70 years, compared with 3.17 years for CNN-based models. However, MAEs increase by 0.4–1.7 years on external cohorts, while within-study improvements associated with multimodal imaging (0.54 years), image harmonization (0.29 years), and covariate integration (0.16 years) remain modest. Strong evidence supports the benefits of large-cohort training for deep learning models and the importance of age-bias correction. Of six hypotheses examined, two are partially supported, one is unsupported, and three remain untestable with the available evidence. Building on these findings, an evidence-derived Harmonization-Aware Generative–Transformer (HAGT) design framework is proposed. The framework integrates harmonization with the age-prediction objective, fuses local and global features through gated cross-attention, constrains age-related bias, and estimates predictive uncertainty. Its computational complexity is also analysed. A gap-to-solution roadmap identifies priorities for clinical translation, including shared external benchmarks, task-coupled harmonization, disease-relevant evaluation, calibrated uncertainty estimates, and prospective validation.