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OpenAlex Citation Counts

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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:

Artificial intelligence: A critical review of applications for lung nodule and lung cancer
Constance de Margerie‐Mellon, Guillaume Chassagnon
Diagnostic and Interventional Imaging (2022) Vol. 104, Iss. 1, pp. 11-17
Open Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review
Sunil Kumar, Harish Kumar, Gyanendra Kumar, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 19
Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes
Zainab Gandhi, Priyatham Gurram, Birendra Amgai, et al.
Cancers (2023) Vol. 15, Iss. 21, pp. 5236-5236
Open Access | Times Cited: 35
Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs
Souhail Bennani, Nor-Eddine Regnard, Jeanne Ventre, et al.
Radiology (2023) Vol. 309, Iss. 3
Closed Access | Times Cited: 28
Artificial intelligence in radiotherapy: Current applications and future trends
P Giraud, Jean‐Emmanuel Bibault
Diagnostic and Interventional Imaging (2024)
Closed Access | Times Cited: 5
Attention-guided CenterNet deep learning approach for lung cancer detection
Hussain Dawood, Marriam Nawaz, Muhammad Ilyas, et al.
Computers in Biology and Medicine (2025) Vol. 186, pp. 109613-109613
Closed Access
Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap
Irene Dankwa‐Mullan, Kingsley Ndoh, Darlington Ahiale Akogo, et al.
Current Oncology Reports (2025)
Closed Access
Leveraging machine learning models for enhanced differentiation of hard-diagnosed lung lesions
B. Ekaterina, Suvorova Svetlana, Pavel Gavrilov, et al.
The European Physical Journal Special Topics (2025)
Closed Access
Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort
I. Hamelink, Marcel van Tuinen, Thomas C. Kwee, et al.
European Radiology (2025)
Open Access
Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
Jon Zabaleta, Borja Aguinagalde, Iker López, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 2, pp. 399-399
Open Access
Leveraging Artificial Intelligence to Predict and Manage Complications in Patients With Multimorbidity: A Literature Review
Sai Praneeth Chaparala, Kaustubh Pathak, Raman Dugyala, et al.
Cureus (2025)
Open Access
Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis
W Liu, You Wu, Zhuozhao Zheng, et al.
Journal of Medical Internet Research (2025) Vol. 27, pp. e64649-e64649
Open Access
Minimally invasive biomarkers for triaging lung nodules—challenges and future perspectives
Waqar Ahmed Afridi, Samandra Hernandez Picos, Juliana Müller Bark, et al.
Cancer and Metastasis Reviews (2025) Vol. 44, Iss. 1
Open Access
Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospective Cohort Study
Fang Nie, Xiufeng Pei, Jiahui Du, et al.
International Journal of General Medicine (2025) Vol. Volume 18, pp. 981-996
Open Access
Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model
Tian Fang, Yongchun Lin, Liming Wang, et al.
Frontiers in Medicine (2025) Vol. 11
Open Access
A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer
Serafeim‐Chrysovalantis Kotoulas, Dionysios Spyratos, Κonstantinos Porpodis, et al.
Cancers (2025) Vol. 17, Iss. 5, pp. 882-882
Open Access
Diagnostic du cancer pulmonaire en imagerie : pièges à connaître
Maria Dobre, Samia Ahmed Fawaz, Caroline Caramella, et al.
Journal d imagerie diagnostique et interventionnelle (2025)
Closed Access
The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide
Amin Zadeh Shirazi, Morteza Tofighi, Alireza Gharavi, et al.
Technology in Cancer Research & Treatment (2024) Vol. 23
Open Access | Times Cited: 4
Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening
Shan Gao, Zexuan Xu, Wanli Kang, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports
Koichiro Yasaka, Jun Kanzawa, Noriko Kanemaru, et al.
Deleted Journal (2024)
Open Access | Times Cited: 4
Künstliche Intelligenz in der Kinderpneumologie – Chancen und unbeantwortete Fragen
Stephanie Dramburg
Klinische Pädiatrie (2025)
Closed Access
DETECT IPN: Real-World Experience with Automated Detection of Incidental Pulmonary Nodules in an All-Comer Population
Johannes Dunsche, Hans‐Ulrich Kauczor, Oyunbileg von Stackelberg, et al.
Open Journal of Radiology (2025) Vol. 15, Iss. 01, pp. 13-25
Open Access
French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)
Daphné Guenoun, Marc Zins, Pierre Champsaur, et al.
Diagnostic and Interventional Imaging (2023) Vol. 105, Iss. 2, pp. 74-81
Closed Access | Times Cited: 11
Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging
Arman Sindhu, Ulhas Jadhav, Babaji Ghewade, et al.
Cureus (2024)
Open Access | Times Cited: 3
CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study
Filippo Crimì, Elena Agostini, Alessandro Toniolo, et al.
Current Oncology (2023) Vol. 30, Iss. 2, pp. 2169-2177
Open Access | Times Cited: 8
An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images
S.-L. Hung, Yao‐Tung Wang, Ming‐Hseng Tseng
Cancers (2023) Vol. 15, Iss. 18, pp. 4655-4655
Open Access | Times Cited: 8
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