Table 1_Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.docx

Study quality was appraised using QUADAS-2, and model reporting transparency was benchmarked against TRIPOD and STARD-AI guidelines. Because of methodological heterogeneity, no meta-analysis or statistical pooling was performed; summary values are unweighted descriptive means presented with observed ranges.</p>Results<p>Conventional ultrasound combined with FNA cytology demonstrated high sensitivity (71–99.6%) but variable specificity (14.1–92%), reflecting persistent diagnostic uncertainty in Bethesda III/IV nodules.

AI-enhanced ultrasound tended to improve specificity and reduce false-positive findings compared with conventional methods, though performance varied considerably across studies. Molecular biomarker platforms showed generally high diagnostic performance, with a descriptive (unweighted) mean sensitivity of 86.8%, specificity 78.5%, and NPV 91.8%, enabling many patients to avoid diagnostic lobectomy. Only a few studies have evaluated integrated AI + biomarker models; these preliminary results suggest potential additive value but require further prospective validation before conclusions can be drawn.</p>Conclusions<p>Molecular biomarker testing and AI-assisted ultrasound each show promise for enhancing diagnostic accuracy in indeterminate thyroid nodules, and early evidence suggests that integrating these modalities could further refine malignancy-risk assessment.

However, the current evidence is preliminary and heterogeneous, and the benefit of combined approaches has not yet been confirmed. Large, multicenter prospective trials with standardized protocols and transparent reporting are needed to confirm these findings and to assess real-world cost-effectiveness before routine clinical adoption.</p>