External validation across institutions, scanners, and follow-up pathways is still required beyond the single-center proof-of-concept.

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Artificial intelligence for traumatic brain injury imaging: a translational review from algorithm development to clinical implementation (2026) · doi

  Limited external validation severely undermines claims of generalizability, as most studies validate models using internal splits from a single institution, which cannot capture the full spectrum of scanner variability, protocol differences, and population diversity encountered in real-world practice.

  Keywords: limited external validation severely undermines claims generalizability validate models using internal splits single institution cannot
• Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator (2026) · doi

  As a single-center retrospective study with only internal validation via random splitting, it is inevitably subject to selection bias and information bias, and external validation was not performed, which may limit the model's external generalizability.

  Keywords: validation bias external single center retrospective internal random splitting inevitably subject selection information performed limit
• A multimodal deep learning-based dynamic prediction model for colorectal cancer liver metastasis (2026) · doi

  External validation across institutions, scanners, and follow-up pathways is still required beyond the single-center proof-of-concept.

  Keywords: external validation across institutions scanners follow pathways still required beyond single center proof concept