Radiomics and AI show promise for cervical cancer radiotherapy but face validation and standardization challenges.

This narrative review evaluates the role of radiomics and artificial intelligence (AI) in precision radiotherapy for women with cervical cancer. The analysis focuses on secondary outcomes including target delineation, treatment response prediction, prognostic stratification, and toxicity risk assessment. No specific study design, sample size, or follow-up duration was reported for the evidence synthesized in this review.

The review indicates that radiomics and AI are promising tools for personalized radiotherapy. However, the main results lack specific numerical data as the primary outcomes were not reported in the source material. The synthesis highlights that most available evidence remains retrospective, with limited prospective validation currently available to support widespread adoption.

Safety and tolerability data were not reported in the reviewed literature. Key limitations identified include imaging heterogeneity, insufficient standardization of methods, and limited model interpretability. These factors contribute to an uncertain impact on clinical decision-making. The review explicitly cautions against overstating technical performance or assuming meaningful improvements in patient outcomes based on current data.

Practice relevance is currently uncertain. Until prospective validation improves and standardization issues are resolved, the integration of these technologies into routine clinical workflows requires careful consideration. The uncertain impact on clinical decision-making suggests that these tools may serve as adjuncts rather than replacements for established clinical judgment at this stage.