Comment on: "A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic...

Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug-drug, drug-disease, and drug-nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely restricted to PubMed with title- and abstract-level filtering, while manual record removal is reported without explicit criteria defining "irrelevant" studies, limiting transparency and reproducibility. Protocol registration, duplicate independent screening, standardized extraction procedures, and formal bias assessment using established frameworks such as ROBIS, PROBAST+AI, and TRIPOD+AI were not clearly reported. The review reports performance metrics such as area under the receiver operating characteristic curve (AUROC), but does not provide a structured framework for interpreting or comparing metrics across heterogeneous datasets, prediction tasks, and evaluation protocols. Because the interpretation of AUROC and precision-recall metrics depends on class prevalence, outcome definition, and the intended prediction task, future reviews should report complementary discrimination metrics, calibration, uncertainty estimates, and external validation rather than assuming that any single metric is universally preferable. Claims of superior model performance should be supported by confidence intervals and statistical comparisons appropriate to the evaluation design, such as paired DeLong testing when applicable. Claims of superior model performance should also be supported by appropriate statistical testing, including methods such as the nonparametric DeLong test. Several conceptual clarifications are also warranted. AI models may prioritize hypotheses but do not replace experimental or clinical validation under current regulatory standards. Furthermore, AUROC should not be conflated with pharmacokinetic area under the curve, and SciBERT should not be characterized as a three-dimensional molecular graph framework. Future reviews should adopt transparent multi-database searches, structured bias assessment, and reproducible reporting practices.