ArticleFrontiers in endocrinology2026
Clinical validation of an artificial intelligence model for thyroid fine-needle aspiration biopsy indication: comparison with TI-RADS systems and human specialists in a Chilean public hospital.
Article in Frontiers in endocrinology, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Ultrasound evaluation of thyroid nodules shows significant interobserver variability, contributing to the overutilization of fine-needle aspiration biopsy (FNA). Standardized systems such as TI-RADS aim to reduce this variability; however, their performance in real-world clinical practice remains heterogeneous. Artificial intelligence (AI) has emerged as a potential tool to support clinical decision-making in this setting. Objective: To evaluate the clinical performance of an artificial intelligence model for recommending FNA in thyroid nodules and to compare it with ACR TI-RADS and Horvath TI-RADS classifications applied by specialists. Materials and methods: A retrospective cross-sectional study evaluating the clinical performance of an artificial intelligence model for FNA recommendation was conducted in adult patients who underwent thyroid ultrasound and FNA between January and December 2021. FNA recommendations generated by an AI model were compared with those issued by an expert radiologist and an expert endocrinologist using ACR TI-RADS and Horvath TI-RADS systems. The reference standard was cytology according to the Bethesda System, considering Bethesda ≥ III as a positive result, as an operational definition aligned with clinical decision-making for further diagnostic evaluation. Diagnostic performance metrics and concordance were calculated. Results: A total of 101 patients were included (89.1% women), with a median age of 61 years and a median nodule size of 2.3 cm (IQR: 1.6-3.3). The AI model showed a sensitivity of 0.88 and a specificity of 0.41. Horvath TI-RADS, applied by an expert radiologist, demonstrated a sensitivity of 0.82 and a specificity of 0.69. Concordance between AI and TI-RADS-based methods were moderate. Conclusion: In a real-world clinical setting, the AI model demonstrated performance comparable to that of human specialists for recommending FNA in thyroid nodules. These findings support its potential role as a complementary decision-support tool, particularly in settings with variability in ultrasound interpretation.
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.