Evidence map›Paper›PMID 39979566›Full record

ArticleEndocrine2025

Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training.

Daham Kim, Yoon-A Hwang, Youngsook Kim, Hye Sun Lee, Eunjung Lee, Hyunju Lee, Jung Hyun Yoon, Vivian Youngjean Park, Miribi Rho, Jiyoung Yoon and 2 more

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Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Daham KimDepartment of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul, Republic of Korea.
Yoon-A HwangDepartment of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul, Republic of Korea.
Youngsook KimDepartment of Internal Medicine, Institute of Endocrine Research, Yonsei University College of Medicine, Seoul, Republic of Korea.
Hye Sun LeeBiostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eunjung LeeSchool of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea.
Hyunju LeeDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Jung Hyun YoonDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Vivian Youngjean ParkDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Miribi RhoDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Jiyoung YoonDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Si Eun LeeDepartment of Radiology, Yongin Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Yongin, Republic of Korea.
Jin Young KwakDepartment of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea. docjin@yuhs.ac.

Funding

National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) 2021R1A2C2007492
6 · The paper itself

Abstract

purposeThis study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thyroid nodules.

methodsConducted from March to December 2022, internal medicine residents underwent three repeated learning sessions with a "learning set" comprising 3000 thyroid nodule images. Diagnostic performances for internal medicine residents were assessed before the study, after every learning session, and for radiology residents before and after one-on-one education, using a "test set," comprising 120 thyroid nodule images. Finally, all residents repeated the same test using artificial intelligence computer-assisted diagnosis (AI-CAD).

resultsTwenty-one internal medicine and eight radiology residents participated. Initially, internal medicine residents had a lower area under the receiver operating characteristic curve (AUROC) than radiology residents (0.578 vs. 0.701, P < 0.001), improving post-learning (0.578 to 0.709, P < 0.001) to a comparable level with radiology residents (0.709 vs. 0.735, P = 0.17). Further improvement occurred with AI-CAD for both group (0.709 to 0.755, P < 0.001; 0.735 to 0.768, P = 0.03).

conclusionThe proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners, such as residents, in thyroid imaging to differentiate benign and malignant thyroid nodules. Additionally, AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.

Indexed as

Artificial IntelligenceInternal MedicineInternship and ResidencyThyroid NoduleAdultClinical CompetenceDiagnosis, Computer-AssistedFemaleHumansMaleRadiologyThyroid NeoplasmsArtificial intelligenceComputer assisted diagnosisDeep learningThyroid noduleUltrasonography

Identifiers

PMID39979566

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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.