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:

The state of the art for artificial intelligence in lung digital pathology
Vidya Sankar Viswanathan, Paula Toro, Germán Corredor, et al.
The Journal of Pathology (2022) Vol. 257, Iss. 4, pp. 413-429
Open Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Explainability and causability in digital pathology
Markus Plass, Michaela Kargl, Tim‐Rasmus Kiehl, et al.
The Journal of Pathology Clinical Research (2023) Vol. 9, Iss. 4, pp. 251-260
Open Access | Times Cited: 39

Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects
Gregory Verghese, Jochen K. Lennerz, Danny Ruta, et al.
The Journal of Pathology (2023) Vol. 260, Iss. 5, pp. 551-563
Open Access | Times Cited: 32

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?
H Evans, David Snead
Histopathology (2023) Vol. 84, Iss. 2, pp. 279-287
Open Access | Times Cited: 19

Unleashing the potential of AI for pathology: challenges and recommendations
Amina Asif, Kashif Rajpoot, Simon Graham, et al.
The Journal of Pathology (2023) Vol. 260, Iss. 5, pp. 564-577
Open Access | Times Cited: 16

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer
Christine McCaffrey, Chowdhury Arif Jahangir, Clodagh Murphy, et al.
Expert Review of Molecular Diagnostics (2024) Vol. 24, Iss. 5, pp. 363-377
Open Access | Times Cited: 7

Advanced 3D In Vitro Lung Fibrosis Models: Contemporary Status, Clinical Uptake, and Prospective Outlooks
Nipun Jain, Bharat Bhushan, M. Natarajan, et al.
ACS Biomaterials Science & Engineering (2024) Vol. 10, Iss. 3, pp. 1235-1261
Closed Access | Times Cited: 5

Application of Artificial Intelligence in Radiological Image Analysis for Pulmonary Disease Diagnosis: A Review of Current Methods and Challenges
Karolina Zalewa, Joanna Olszak, Wojciech Kapłan, et al.
Journal of Education Health and Sport (2025) Vol. 77, pp. 56893-56893
Open Access

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist
Paul Hofman, Iordanis Ourailidis, Eva Romanovsky, et al.
Lung Cancer (2025) Vol. 200, pp. 108110-108110
Closed Access

Comparative performance of PDL1 scoring by pathologists and AI algorithms
Markus Plass, G. Olteanu, Sanja Đačić, et al.
Histopathology (2025)
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

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma
Pingjun Chen, Frank Rojas, Xin Hu, et al.
Modern Pathology (2023) Vol. 36, Iss. 12, pp. 100326-100326
Open Access | Times Cited: 15

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives
Germán Corredor, Satvika Bharadwaj, Tilak Pathak, et al.
Clinical Breast Cancer (2023) Vol. 23, Iss. 8, pp. 800-812
Open Access | Times Cited: 12

Deciphering Lung Adenocarcinoma Progression Through Molecular Insights: The Challenges and Potential of Radiomics and Machine Learning
Ritu R. Gill, Despina Kontos
Journal of Thoracic Oncology (2025) Vol. 20, Iss. 1, pp. 20-22
Closed Access

Enhancing early lung cancer detection with MobileNet: A comprehensive transfer learning approach
Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, J A García-Rodríguez, et al.
Franklin Open (2025), pp. 100222-100222
Open Access

Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma
Linshan Zhou, Mu Yang, Jiadi Luo, et al.
PLoS ONE (2025) Vol. 20, Iss. 1, pp. e0315893-e0315893
Open Access

RMD-Net: A Deep Learning Framework for Automated IHC Scoring of Lung Cancer IL-24
Zihao He, Dongyao Jia, Yinan Shi, et al.
Mathematics (2025) Vol. 13, Iss. 3, pp. 417-417
Open Access

Advanced Techniques for Cancer Research with Multimodal Fusion and Deep Learning
Mallikarjun Shankar, Lekshmi Kalinathan, J. Senthil Murugan
Communications in computer and information science (2025), pp. 243-251
Closed Access

Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Leonid Mill, Oliver Aust, Jochen A. Ackermann, et al.
Communications Medicine (2025) Vol. 5, Iss. 1
Open Access

Thoracic Ultrasound for Pre-Procedural Dynamic Assessment of Non-Expandable Lung: A Non-Invasive, Real-Time and Multifaceted Diagnostic Tool
Gianluca Marchi, Federico Cucchiara, Alessio Gregori, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 6, pp. 2062-2062
Open Access

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram
Dexin Chen, Jianbo Lai, Jiaxin Cheng, et al.
iScience (2023) Vol. 26, Iss. 3, pp. 106246-106246
Open Access | Times Cited: 12

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model
Mehdi Nikfar, Hao‐Yang Mi, Chang Gong, et al.
Cancers (2023) Vol. 15, Iss. 10, pp. 2750-2750
Open Access | Times Cited: 11

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