Thyroid disease

An agentic, no-code artificial intelligence workflow for developing a thyroid nodule ultrasound malignancy classifier

J Clin Endocrinol MetabPublished Diagnostic accuracy study

Authors' conclusion

An agentic, no-code AI workflow produced a calibrated, externally validated thyroid nodule classifier, supporting accessible, reproducible, and independently testable medical AI development.

Study context

Population
Thyroid nodules on US; internal test set from TN5000 dataset; external cohort of nodules from the University of Colorado spanning multiple cancer histologies
Participants
232 external validation nodules
Comparison
Agentic no-code artificial intelligence (AI)-developed ResNet-18 classifier vs previously published benchmark classifier
Outcome
Diagnostic performance for thyroid nodule malignancy on external validation
Methodological detailsretrospective, computational, external validation
  • Retrospective
  • Computational
  • External validation

Performance

As reported in the abstract

Test or modelAUROCSensitivitySpecificityPPVNPV
Agentic model0.90 (95% CI, 0.85-0.93)0.920.680.520.96
Benchmark model0.83— not stated in the abstract— not stated in the abstract— not stated in the abstract— not stated in the abstract

AUROC

Agentic model0.90 (95% CI, 0.85-0.93)
Benchmark model0.83

Sensitivity

Agentic model0.92
Benchmark model— not stated in the abstract

Specificity

Agentic model0.68
Benchmark model— not stated in the abstract

PPV

Agentic model0.52
Benchmark model— not stated in the abstract

NPV

Agentic model0.96
Benchmark model— not stated in the abstract

— not stated in the abstract

  • Agentic model vs Benchmark model: paired DeLong P = .03

Further findings

OutcomeAgentic model
Diagnostic performance on internal test set
AUROC0.94 (95% CI, 0.92-0.95)
Sensitivity0.90
Specificity0.81

Missed cancers

Missed cancers clustered among follicular pattern tumors

Original publication

An agentic, no-code artificial intelligence workflow for developing a thyroid nodule ultrasound malignancy classifier. J Clin Endocrinol Metab. Published 29 September 2026. doi:10.1210/clinem/dgag351

Composed by AI from the published abstract and checked against it. How Briefs are made →

Get them every week

New studies on this topic in My Week and in your Monday e-mail.

We e-mail you a sign-in link and a code. No password, no spam.