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A systematic review of 102 AI prediction studies showing that most lack external validation, prospective evidence, or workflow integration; none demonstrate interventional benefit for treatment tailoring in breast cancer neoadjuvant therapy.
Single-center machine learning model with reasonable test-set performance (AUC 0.894) and independent temporal validation, but surrogate endpoints (biochemical markers and imaging classification) rather than hard clinical outcomes, and no comparison to standard risk stratification.
This is a methodological comparison study using simulation and observational radiomics data to evaluate machine learning algorithms for confounder selection; it does not test a clinical hypothesis or report a patient outcome.
This is a narrative review synthesizing existing literature on AI applications in endometrial cancer; it does not report original empirical results, primary endpoints, or comparative efficacy data from controlled studies, and explicitly acknowledges that prospective clinical evidence remains insufficient.
Future Journal of Pharmaceuticals and Health Sciences
This is a narrative review that raises questions about AI applications in cancer diagnosis and treatment without reporting original data, controlled comparisons, or quantified evidence of clinical outcomes.
A narrative mini-review presenting conceptual applications of AI in cancer plasticity research; no empirical data, clinical outcomes, or comparative evidence reported.
A White Paper outlining strategic recommendations and a roadmap for international registry collaboration in lymphoma, not reporting original research findings or clinical trial data.
Single-arm algorithm development study on a public dataset with surrogate imaging endpoints (segmentation accuracy metrics) and no clinical validation or prospective comparison.