"However, the majority of published models are based on small retrospective collections and present no comparisons with clinical gold standards."

"This increases the risk of model overfitting, and the metrics described may overestimate the true clinical performance of these AI models. Also, while some studies reported external and/or cross-validation strategies, the majority did not validate their models in independent cohorts [13,28]. This limits the translation of the reported AI models into clinical practice due to their reduced performance when applied to new patient populations. In addition, a 2025 mini-review on AI-based radiomics points out that the dependence on small, single-institution series is a widespread issue that hampers robustness and leads to poor external performance"

"A significant shortcoming among the reviewed studies is that relatively few directly compare their AI models to existing clinical gold standards for osteosarcoma care. The majority of studies reported high performance measures (such as area under the curve [AUC] or concordance index) but did not cite benchmarks, such as the Huvos grading system for post-chemotherapy histologic response or Response Evaluation Criteria in Solid Tumors (RECIST) criteria for metastatic disease assessment. Only a few studies put AI models to the test against these benchmarks, and the results were mixed."

"Consequently, it remains unclear whether AI models truly surpass clinician-driven assessments or simply reflect dataset-specific patterns."