ArticleGland surgery2026
Artificial intelligence-based systems as auxiliary tools for thyroid nodule malignancy detection on ultrasound: a systematic review and meta-analysis of diagnostic accuracy.
Article in Gland surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI)-based systems are valuable tools in thyroid nodule diagnosis, but clinical controversy remains regarding diagnostic inconsistency and specificity. This meta-analysis evaluated the diagnostic accuracy of AI-assisted ultrasound systems for detecting malignant thyroid nodules compared to radiologists and risk stratification systems. Methods: Guided by the PICOS (Population, Intervention, Comparison, Outcome, Study Design) framework, a systematic search across PubMed, Semantic Scholar, and OpenAlex identified 69 studies using histopathological or cytological reference standards. Quality and Risk of Bias were assessed via an AI-tailored QUADAS-2 tool. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using bivariate random-effects meta-analysis. Results: The 69 studies evaluated >350,000 nodules. AI-based systems demonstrated high diagnostic accuracy: pooled sensitivity 89% [95% confidence interval (CI): 87-91%], specificity 84% (95% CI: 80-88%), and AUC 0.93 (95% CI: 0.91-0.95). In direct comparisons, AI systems achieved comparable or superior performance to experienced radiologists (AUC 0.90-0.95 Conclusions: AI systems achieve high diagnostic accuracy for thyroid nodule malignancy, demonstrating performance comparable or superior to radiologists and risk stratification frameworks. As adjunctive tools, they safely reduce unnecessary biopsies and improve diagnostic consistency. Future clinical integration must prioritize multimodal data and radiomics to maximize utility.
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.