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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:

The explainability paradox: Challenges for xAI in digital pathology
Theodore Evans, Carl Orge Retzlaff, Christian Geißler, et al.
Future Generation Computer Systems (2022) Vol. 133, pp. 281-296
Open Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

Survey of Explainable AI Techniques in Healthcare
Ahmad Chaddad, Jihao Peng, Jian Xu, et al.
Sensors (2023) Vol. 23, Iss. 2, pp. 634-634
Open Access | Times Cited: 248
Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research
AKM Bahalul Haque, A.K.M. Najmul Islam, Patrick Mikalef
Technological Forecasting and Social Change (2022) Vol. 186, pp. 122120-122120
Open Access | Times Cited: 99
What is Human-Centered about Human-Centered AI? A Map of the Research Landscape
Tara Capel, Margot Brereton
(2023), pp. 1-23
Open Access | Times Cited: 62
Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis
Federico Cabitza, Andrea Campagner, Luca Ronzio, et al.
Artificial Intelligence in Medicine (2023) Vol. 138, pp. 102506-102506
Open Access | Times Cited: 43
Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists
Carl Orge Retzlaff, Alessa Angerschmid, Anna Saranti, et al.
Cognitive Systems Research (2024) Vol. 86, pp. 101243-101243
Open Access | Times Cited: 30
Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities
Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, et al.
Journal of Artificial Intelligence Research (2024) Vol. 79, pp. 359-415
Open Access | Times Cited: 26
Deep learning in cancer genomics and histopathology
Michaela Unger, Jakob Nikolas Kather
Genome Medicine (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 23
Modeling adoption of intelligent agents in medical imaging
Francisco Maria Calisto, Nuno Nunes, Jacinto C. Nascimento
International Journal of Human-Computer Studies (2022) Vol. 168, pp. 102922-102922
Closed Access | Times Cited: 64
Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making
Oskar Wysocki, Jessica Katharine Davies, Markel Vigo, et al.
Artificial Intelligence (2022) Vol. 316, pp. 103839-103839
Open Access | Times Cited: 63
Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European In Vitro Diagnostic Regulation
Heimo Müller, Andreas Holzinger, Markus Plass, et al.
New Biotechnology (2022) Vol. 70, pp. 67-72
Open Access | Times Cited: 57
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology
André Homeyer, Christian Geißler, Lars Ole Schwen, et al.
Modern Pathology (2022) Vol. 35, Iss. 12, pp. 1759-1769
Open Access | Times Cited: 42
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: 38
Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity
Marek Pawlicki, Aleksandra Pawlicka, Rafał Kozik, et al.
Neurocomputing (2024) Vol. 590, pp. 127759-127759
Open Access | Times Cited: 13
Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task
Weina Jin, Mostafa Fatehi, Ru Guo, et al.
Artificial Intelligence in Medicine (2024) Vol. 148, pp. 102751-102751
Open Access | Times Cited: 11
The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination
Mohammad Hosein Rezazade Mehrizi, Ferdinand Mol, Marcel Peter, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
Digitization of Pathology Labs: A Review of Lessons Learned
Lars Ole Schwen, Tim‐Rasmus Kiehl, Rita Carvalho, et al.
Laboratory Investigation (2023) Vol. 103, Iss. 11, pp. 100244-100244
Open Access | Times Cited: 17
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: 17
Extended correlation functions for spatial analysis of multiplex imaging data
Joshua A. Bull, Eoghan J. Mulholland, Simon J. Leedham, et al.
Biological Imaging (2024) Vol. 4
Open Access | Times Cited: 6
Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications
Sayda Umma Hamida, Mohammad Jabed Morshed Chowdhury, Narayan Ranjan Chakraborty, et al.
Big Data and Cognitive Computing (2024) Vol. 8, Iss. 11, pp. 149-149
Open Access | Times Cited: 6
Histopathology image classification: highlighting the gap between manual analysis and AI automation
Refika Sultan Doğan, Bülent Yılmaz
Frontiers in Oncology (2024) Vol. 13
Open Access | Times Cited: 5
Human Centred Explainable AI Decision-Making in Healthcare
Catharina Margaretha van Leersum, Clara Maathuis
Journal of Responsible Technology (2025), pp. 100108-100108
Open Access
Trustworthy, self-fulfilling, non-redundant proficiency tests for metrological challenges in medical diagnostics
Tomasz Sołtysiński
Measurement Sensors (2025), pp. 101646-101646
Open Access
Personalized explanations for clinician-AI interaction in breast imaging diagnosis by adapting communication to expertise levels
Francisco Maria Calisto, João Abrantes, Carlos Santiago, et al.
International Journal of Human-Computer Studies (2025), pp. 103444-103444
Open Access
User Experience and Trust Improvement Evaluation on Healthcare oriented Explainable Artificial Intelligence Interventions: Systematic Review (Preprint)
Samantha Cramer, Elina Gastreich-de-Llanes, Jonas Bäcker, et al.
(2025)
Closed Access
Image‐Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches
Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, et al.
Applied Computational Intelligence and Soft Computing (2025) Vol. 2025, Iss. 1
Open Access
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