Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.
Authors
Affiliations (8)
Affiliations (8)
- Florida International University Herbert Wertheim College of Medicine, Miami, FL, United States.
- Midwestern University College of Health Sciences, Glendale, AZ, United States.
- Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, United States.
- University of California, Irvine, Irvine, CA, United States.
- Rocky Vista University, Parker, CO, United States.
- Michigan State University College of Osteopathic Medicine, East Lansing, MI, United States.
- Rocky Vista University Montana College of Osteopathic Medicine, Billings, MT, United States.
- North Atlanta Ear, Nose, and Throat and Allergy, Atlanta, GA, United States.
Abstract
Indeterminate thyroid nodules (Bethesda III/IV) are difficult to manage: ultrasound and fine-needle aspiration (FNA) often yield non-definitive results, so many patients undergo diagnostic lobectomy for lesions that prove benign. The combination of AI-enhanced ultrasound and molecular biomarker platforms provides complementary diagnostic capabilities. However, evidence on their combined application remains limited, derives from a small number of early studies, and has not yet been validated in large or prospective cohorts. A systematic search of PubMed (2012-2025) identified studies evaluating the diagnostic performance of conventional ultrasound + FNA, AI-assisted ultrasound, molecular biomarker platforms (Afirma, ThyroSeq, ThyGeNEXT, ThyraMIR), and integrated multimodal approaches combining AI-enhanced ultrasound radiomics with molecular biomarker testing in indeterminate thyroid nodules. The search was limited to English-language publications. After applying inclusion and exclusion criteria, 49 studies were included. Extracted outcomes included sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). 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. 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. 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.