ReviewCancers2022
Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Head-to-head comparison of American, European, and Asian TIRADSs in thyroid nodule assessment: systematic review and meta-analysis.European thyroid journal · 2024Pooled it
- Thyroid and renal cancers: A bidirectional association.Frontiers in oncology · 2022Pooled it
- Pooled it
- Diagnostic performance and generalizability of a clinical-ultrasound radiomics model for predicting extrathyroidal extension in thyroid carcinoma: a retrospective study.Annals of medicine · 2026Article
- Adjusted Risk Stratification of Indeterminate Thyroid Nodules Using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS).Cancer medicine · 2026Article
- Artificial intelligence in thyroid nodules and cancer: clinical validation and real-world performance.Endocrine connections · 2026Article
- Application of AmCAD-UT in Active Surveillance of Papillary Thyroid Microcarcinoma and Suspicious Subcentimeter Thyroid Nodules: A Prospective Agreement Study.World journal of surgery · 2026Article
- Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients.Scientific reports · 2026Article
- Current Evidence, Selective Indications, and the Role of Lymph-Node Assessment in Intraoperative Frozen Section in Thyroid Cancer Surgery: A Literature Review.Journal of clinical medicine · 2026Review
- Machine learning-based classification of papillary thyroid carcinoma versus multinodular goiter using preoperative laboratory and cytology data.BMC endocrine disorders · 2026Article
- Appropriateness of Thyroid Nodule Cancer Risk Assessment and Management Recommendations Provided by Large Language Models.Journal of imaging informatics in medicine · 2025Article
- Spatial-Temporal Information Fusion for Thyroid Nodule Segmentation in Dynamic Contrast-Enhanced MRI: A Novel Approach.Journal of imaging informatics in medicine · 2025Article
- Quantitative Ultrasound Grayscale Analysis and Size of Benign and Malignant Solid Thyroid Nodules.Tomography (Ann Arbor, Mich.) · 2025Article
- Review
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios.BMC endocrine disorders · 2025Article
- Prediction of thyroid malignancy risk using clinical and ultrasonography features and a machine learning approach.European radiology · 2025Article
- Multimodal model enhances qualitative diagnosis of hypervascular thyroid nodules: integrating radiomics and deep learning features based on B-mode and PDI images.Gland surgery · 2025Article
- Comparative Analysis of Diagnostic Performance Between Elastography and AI-Based S-Detect for Thyroid Nodule Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Thyroid Nodule Characterization: Which Thyroid Imaging Reporting and Data System (TIRADS) Is More Accurate? A Comparison Between Radiologists with Different Experiences and Artificial Intelligence Software.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.BMJ open · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.