This study explores how machine learning models can assist radiologists in identifying extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC). Following PRISMA 2020 guidelines, the researchers conducted a systematic review and meta-analysis across five databases: MEDLINE (Ovid), EMBASE, Cochrane, Scopus, and Web of Science. Of the 57 articles identified, only six met all the eligibility criteria and directly compared assessments made by machine learning algorithms to those by trained neuroradiologists or radiologists. The researchers main outcomes of interest were accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) of the predictions made by MLAs or radiologists. Across pooled data, MLAs had better specificity, sensitivity, NPV, PPV, and AUC as compared to radiologists. The AUC for AI models was 0.91, much higher than the 0.65 for human readers. These findings show that AI has a strong potential to enhance clinical decision making, but further steps are needed for full clinical integration. The researchers conclude that AI should not replace, but rather complement human expertise in diagnosing ENE within HNCSS.