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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:

Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis
Andrew James Codlin, Thang Phuoc Dao, Luan Nguyen Quang Vo, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all?
Ruvandhi R. Nathavitharana, Alberto L. García‐Basteiro, Morten Rühwald, et al.
EBioMedicine (2022) Vol. 78, pp. 103939-103939
Open Access | Times Cited: 58
The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination
Coralie Geric, Zhi Zhen Qin, Claudia M. Denkinger, et al.
The International Journal of Tuberculosis and Lung Disease (2023) Vol. 27, Iss. 5, pp. 367-372
Open Access | Times Cited: 34
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: 34
Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study
Sifrash Meseret Gelaw, Sandra V. Kik, Morten Rühwald, et al.
PLOS Global Public Health (2023) Vol. 3, Iss. 7, pp. e0000402-e0000402
Open Access | Times Cited: 23
AI for Detection of Tuberculosis: Implications for Global Health
Eui Jin Hwang, Won Gi Jeong, Pierre‐Marie David, et al.
Radiology Artificial Intelligence (2024) Vol. 6, Iss. 2
Open Access | Times Cited: 10
Computer-Aided Detection (CAD) Software Versus Radiologists from Multiple Countries: A Comparison of Tuberculosis Detection from Chest X-Rays
Zhi Zhen Qin, Martie van der Walt, Sizulu Moyo, et al.
Research Square (Research Square) (2025)
Closed Access | Times Cited: 1
Clinical evaluation of computer-aided digital x-ray detection of pulmonary tuberculosis during community-based screening or active case-finding: a case–control study
Alex Scott, Tahlia Perumal, Anil Pooran, et al.
The Lancet Global Health (2025) Vol. 13, Iss. 3, pp. e517-e527
Closed Access | Times Cited: 1
Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health
Julien Onno, Faiz Ahmad Khan, Amrita Daftary, et al.
Social Science & Medicine (2023) Vol. 327, pp. 115949-115949
Closed Access | Times Cited: 17
Applicability of artificial intelligence-based computer-aided detection (AI–CAD) for pulmonary tuberculosis to community-based active case finding
Kosuke Okada, Norio Yamada, Kiyoko Takayanagi, et al.
Tropical Medicine and Health (2024) Vol. 52, Iss. 1
Open Access | Times Cited: 6
Diagnostic Accuracy of Computer-Aided Detection During Active Case Finding for Pulmonary Tuberculosis in Africa: A Systematic Review and Meta-analysis
Alex Scott, Tahlia Perumal, Ameer Hohlfeld, et al.
Open Forum Infectious Diseases (2024) Vol. 11, Iss. 2
Open Access | Times Cited: 6
Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software
Zhi Zhen Qin, Martie van der Walt, Sizulu Moyo, et al.
The Lancet Digital Health (2024) Vol. 6, Iss. 9, pp. e605-e613
Open Access | Times Cited: 6
The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis
Shufan Liang, Jiechao Ma, Gang Wang, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 26
World TB Day 2022: Revamping and Reshaping Global TB Control Programs by Advancing Lessons learnt from the COVID-19 pandemic
Eskild Petersen, Seif Al-Abri, Jeremiah Chakaya, et al.
International Journal of Infectious Diseases (2022) Vol. 124, pp. S1-S3
Open Access | Times Cited: 24
Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches
My Bui, Minh Hoàng Hà, Hoang Giang Pham, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 23
Early user experience and lessons learned using ultra-portable digital X-ray with computer-aided detection (DXR-CAD) products: A qualitative study from the perspective of healthcare providers
Zhi Zhen Qin, Rachael Barrett, María del Mar Castro, et al.
PLoS ONE (2023) Vol. 18, Iss. 2, pp. e0277843-e0277843
Open Access | Times Cited: 14
AI-Based CXR First Reading: Current Limitations to Ensure Practical Value
Yuriy А. Vasilev, Anton V. Vladzymyrskyy, Olga V. Omelyanskaya, et al.
Diagnostics (2023) Vol. 13, Iss. 8, pp. 1430-1430
Open Access | Times Cited: 13
Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications
Jonathan A. Liu, Issac Y. Yang, Emily B. Tsai
American Journal of Roentgenology (2022) Vol. 219, Iss. 5, pp. 703-712
Closed Access | Times Cited: 19
Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis
Jacob Creswell, Luan Nguyen Quang Vo, Zhi Zhen Qin, et al.
BMC Global and Public Health (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 12
Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria
Stephen John, Suraj Abdulkarim, Salisu Usman, et al.
BMC Global and Public Health (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 11
Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population
Ahmed Maiter, Katherine Hocking, S. Matthews, et al.
BMJ Open (2023) Vol. 13, Iss. 11, pp. e077348-e077348
Open Access | Times Cited: 11
Finding the missed millions: innovations to bring tuberculosis diagnosis closer to key populations
Rachel L. Byrne, Tom Wingfield, Emily R. Adams, et al.
BMC Global and Public Health (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 4
A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia
Sam Linsen, Aurélie Kamoun, Andrews Gunda, et al.
PLOS Digital Health (2025) Vol. 4, Iss. 1, pp. e0000535-e0000535
Open Access
Comparative analysis of the impact of portable digital X-ray on TB screening in hard-to-reach areas in Nigeria
Sani Useni, Bethrand Odume, Muhammad Tukur, et al.
Public Health Action (2025) Vol. 15, Iss. 1, pp. 17-20
Closed Access
Accuracy of computer‐aided chest x‐ray interpretation for tuberculosis screening in people with diabetes mellitus: A systematic review
Reagan Emoru, Lucy Elauteri Mrema, Nyanda Elias Ntinginya, et al.
Tropical Medicine & International Health (2025)
Open Access
Performance of Computer‐Aided Detection Software in Tuberculosis Case Finding in Township Health Centers in China
Xuefang Cao, Boxuan Feng, Bin Zhang, et al.
Chronic Diseases and Translational Medicine (2025)
Open Access
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