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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules
Johnson Thomas, Tracy Haertling
Thyroid (2020) Vol. 30, Iss. 6, pp. 878-884
Open Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study
Sui Peng, Yihao Liu, Weiming Lv, et al.
The Lancet Digital Health (2021) Vol. 3, Iss. 4, pp. e250-e259
Open Access | Times Cited: 241
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
Yassine Habchi, Yassine Himeur, Hamza Kheddar, et al.
Systems (2023) Vol. 11, Iss. 10, pp. 519-519
Open Access | Times Cited: 46
Contemporary Management of Thyroid Nodules
Kristen Kobaly, Caroline S. Kim, Susan J. Mandel
Annual Review of Medicine (2021) Vol. 73, Iss. 1, pp. 517-528
Closed Access | Times Cited: 75
Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?
Salvatore Sorrenti, Vincenzo Dolcetti, Maija Radziņa, et al.
Cancers (2022) Vol. 14, Iss. 14, pp. 3357-3357
Open Access | Times Cited: 68
Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations
Ying Zou, Yan Shi, Fang Sun, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 225, pp. 107038-107038
Open Access | Times Cited: 40
The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update
Maksymilian Ludwig, Bartłomiej Ludwig, Agnieszka Mikuła, et al.
Cancers (2023) Vol. 15, Iss. 3, pp. 708-708
Open Access | Times Cited: 34
Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions
David Toro-Tobón, Ricardo Loor-Torres, Mayra Durán, et al.
Thyroid (2023) Vol. 33, Iss. 8, pp. 903-917
Closed Access | Times Cited: 28
Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives
Lingrui Li, Bo Du, Hanqing Liu, et al.
Frontiers in Oncology (2021) Vol. 10
Open Access | Times Cited: 42
Machine intelligence in non-invasive endocrine cancer diagnostics
Nicole M. Thomasian, Ihab R. Kamel, Harrison X. Bai
Nature Reviews Endocrinology (2021) Vol. 18, Iss. 2, pp. 81-95
Open Access | Times Cited: 42
Artificial Intelligence for Evaluation of Thyroid Nodules: A Primer
Franklin N. Tessler, Johnson Thomas
Thyroid (2022) Vol. 33, Iss. 2, pp. 150-158
Closed Access | Times Cited: 30
Assessment of the risk of malignancy in Bethesda III thyroid nodules: a comprehensive review
Karthik Rao, Gregory W. Randolph, Fernando López, et al.
Endocrine (2024) Vol. 85, Iss. 2, pp. 473-492
Closed Access | Times Cited: 6
Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
Vijay Vyas Vadhiraj, Andrew J. Simpkin, James O’Connell, et al.
Medicina (2021) Vol. 57, Iss. 6, pp. 527-527
Open Access | Times Cited: 37
Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP
Yan Shi, Ying Zou, Jihua Liu, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 27
Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study
Pawitchaya Namsena, Dittapong Songsaeng, Chadaporn Keatmanee, et al.
Quantitative Imaging in Medicine and Surgery (2024) Vol. 14, Iss. 5, pp. 3676-3694
Open Access | Times Cited: 5
An integrated AI model to improve diagnostic accuracy of ultrasound and output known risk features in suspicious thyroid nodules
Juan Wang, Jue Jiang, Dong Zhang, et al.
European Radiology (2021) Vol. 32, Iss. 3, pp. 2120-2129
Closed Access | Times Cited: 30
Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis
Eoin F. Cleere, Matthew G. Davey, Shane O’Neill, et al.
Diagnostics (2022) Vol. 12, Iss. 4, pp. 794-794
Open Access | Times Cited: 20
A Comparative Study and Systematic Analysis of XAI Models and their Applications in Healthcare
Jyoti Gupta, K. R. Seeja
Archives of Computational Methods in Engineering (2024)
Closed Access | Times Cited: 4
Explainable artificial intelligence and machine learning algorithms for classification of thyroid disease
Priyanka Kumari, Baljinder Kaur, Manik Rakhra, et al.
Deleted Journal (2024) Vol. 6, Iss. 7
Open Access | Times Cited: 4
Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training
Daham Kim, Yoon-a Hwang, Young‐Sook Kim, et al.
Endocrine (2025)
Closed Access
Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant
Nikita Pozdeyev, Manjiri Dighe, Martin Barrio, et al.
The Journal of Clinical Endocrinology & Metabolism (2023) Vol. 109, Iss. 2, pp. 402-412
Open Access | Times Cited: 10
Use of artificial intelligence and machine learning for estimating malignancy risk of thyroid nodules
Johnson Thomas, Gregory A. Ledger, Chaitanya Mamillapalli
Current Opinion in Endocrinology Diabetes and Obesity (2020) Vol. 27, Iss. 5, pp. 345-350
Closed Access | Times Cited: 25
NTRK fusions in thyroid cancer: Pathology and clinical aspects
Yanhui Ma, Qi Zhang, Kexin Zhang, et al.
Critical Reviews in Oncology/Hematology (2023) Vol. 184, pp. 103957-103957
Closed Access | Times Cited: 9
Towards Trust of Explainable AI in Thyroid Nodule Diagnosis
Truong Thanh Hung Nguyen, Van Binh Truong, Vo Thanh Khang Nguyen, et al.
Studies in computational intelligence (2023), pp. 11-26
Closed Access | Times Cited: 9
A knowledge-interpretable multi-task learning framework for automated thyroid nodule diagnosis in ultrasound videos
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, et al.
Medical Image Analysis (2023) Vol. 91, pp. 103039-103039
Closed Access | Times Cited: 9
External validation of AIBx, an artificial intelligence model for risk stratification, in thyroid nodules
Kristine Zøylner Swan, Johnson Thomas, Viveque Egsgaard Nielsen, et al.
European Thyroid Journal (2022) Vol. 11, Iss. 2
Open Access | Times Cited: 13
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