SynthesisEndocrine, metabolic & immune disorders drug targets2024
The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis.
Synthesis in Endocrine, metabolic & immune disorders drug targets, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 4 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
10 citing papers in PubMed, 4 syntheses or guidelines pooled it, 9 citations in OpenAlex.
- Bibliometric and Latent Dirichlet Allocation (LDA) analysis of artificial intelligence for thyroid nodules.Frontiers in endocrinology · 2026Pooled it
- The global academic distribution and changes in research hotspots of artificial intelligence in inflammatory bowel disease since 2000.Frontiers in medicine · 2025Pooled it
- Mapping the giants: a bibliometric analysis of the top 100 most-cited thyroid nodules studies.Frontiers in medicine · 2025Pooled it
- Worldwide research trends in Crohn's disease treatment over the past 2 decades: a bibliometric analysis.Frontiers in pharmacology · 2024Pooled it
- Present research and emerging trends in neurogenic bladder research: a bibliometric and visualization analysis (2015-2025).Translational andrology and urology · 2026Article
- Bilateral disease in the classic subtype of papillary thyroid carcinoma: clinical significance and development of an artificial intelligence-based multimodal prediction model.Frontiers in endocrinology · 2026Article
- Navigating the complexity of current interventional radiological approaches for distant metastases in thyroid cancer.Frontiers in endocrinology · 2025Review
- Mapping the Evolution of Thyroid Ultrasound Research: A 30-year Bibliometric AnalysisCurrent medical imaging · 2025Review
- Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis.Interactive journal of medical research · 2024Article
- Global research landscape on artificial intelligence in echocardiography from 1997 to 2024: Bibliometric analysis.Digital healthArticle
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThyroid nodules are common lesions in benign and malignant thyroid diseases. More and more studies have been conducted on the feasibility of artificial intelligence (AI) in the detection, diagnosis, and evaluation of thyroid nodules. The aim of this study was to use bibliometric methods to analyze and predict the hot spots and frontiers of AI in thyroid nodules.
methodsArticles on the application of artificial intelligence in thyroid nodules were retrieved from the Web of Science core collection database. A website (https://bibliometric.com/), VOSviewer and CiteSpace software were used for bibliometric analyses. The collaboration maps of countries and institutions were analyzed. The cluster and timeline view based on cocitation references and keywords citation bursts visualization map were generated.
resultsThe study included 601 papers about AI in thyroid nodules. China contributed to more than half (52.41%) of these publications. The cluster view and timeline view of co-citation references were assembled into 9 clusters, "AI", "deep learning", "papillary thyroid carcinoma", "radiomics", "ultrasound image", "biomarkers", "medical image segmentation", "central lymph node metastasis (CLNM)", and "self-organizing auto-encoder". The "AI", "radiomics", "medical image segmentation", "deep learning", and "CLNM", emerging in the last 10 years and continuing until recent years.
conclusionAn increasing number of scholars were devoted to this field. The potential future research hotspots include risk factor assessment and CLNM prediction of thyroid carcinoma based on radiomics and deep learning, automatic segmentation based on medical images (especially ultrasound images).
Indexed as
Identifiers
What Socratic 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 Socratic graph.