ReviewCancers2021
Artificial Intelligence in Thyroid Field-A Comprehensive Review.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review.The Journal of clinical endocrinology and metabolism · 2024Pooled it
- Syncretic Grad-CAM Integrated ViT-CNN Hybrids with Inherent Explainability for Early Thyroid Cancer Diagnosis from Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.Biomolecules & therapeutics · 2026Review
- Assessing agreement with a single-center expert consensus: artificial intelligence-assisted teleultrasound for thyroid nodules categorized as C-TIRADS 4A or higher.Frontiers in endocrinology · 2026Article
- A comparative study on advanced predictive modeling of thyroid cancer recurrence using multi algorithmic machine learning frameworks.Scientific reports · 2025Article
- Appropriateness of Thyroid Nodule Cancer Risk Assessment and Management Recommendations Provided by Large Language Models.Journal of imaging informatics in medicine · 2025Article
- Variability of ChatGPT in Interpreting the Lexicon of ACR-TIRADS, EU-TIRADS, and K-TIRADS.Diagnostics (Basel, Switzerland) · 2025Article
- An interpretable multimodal machine learning model for predicting malignancy of thyroid nodules in low-resource scenarios.BMC endocrine disorders · 2025Article
- Article
- Role of Artificial Intelligence in Thyroid Cancer Diagnosis.Journal of clinical medicine · 2025Review
- Thy-DAMP: deep artificial neural network model for prediction of thyroid cancer mortality.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images.PeerJ. Computer science · 2025Article
- Review
- Current status of artificial intelligence analysis for the diagnosis of gallbladder diseases using ultrasonography: a scoping review.Translational gastroenterology and hepatology · 2025Review
- A novel deep learning model based on multimodal contrast-enhanced ultrasound dynamic video for predicting occult lymph node metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Performance of ACR-TIRADS in assessing thyroid nodules does not vary according to patient age.Hormones (Athens, Greece) · 2024Article
- Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.BMC medical informatics and decision making · 2024Article
- Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.BMC cancer · 2024Article
- Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Exploring the diagnostic performance of machine learning in prediction of metabolic phenotypes focusing on thyroid function.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.
Indexed as
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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.