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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:

Explainable AI Methods - A Brief Overview
Andreas Holzinger, Anna Saranti, Christoph Molnar, et al.
Lecture notes in computer science (2022), pp. 13-38
Open Access | Times Cited: 232

Showing 1-25 of 232 citing articles:

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
A. S. Albahri, Ali M. Duhaim, Mohammed A. Fadhel, et al.
Information Fusion (2023) Vol. 96, pp. 156-191
Closed Access | Times Cited: 349
Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 13863-13895
Open Access | Times Cited: 90
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, et al.
Advanced Intelligent Systems (2024)
Open Access | Times Cited: 58
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ahmad Chaddad, Qizong Lu, Jiali Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 4, pp. 859-876
Open Access | Times Cited: 45
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: 45
Applying large language models and chain-of-thought for automatic scoring
Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, et al.
Computers and Education Artificial Intelligence (2024) Vol. 6, pp. 100213-100213
Open Access | Times Cited: 38
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Hubert Baniecki, Przemysław Biecek
Information Fusion (2024) Vol. 107, pp. 102303-102303
Open Access | Times Cited: 37
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
Long Chen, Oleg Sinavski, Jan Hünermann, et al.
(2024), pp. 14093-14100
Open Access | Times Cited: 36
Predicting Parkinson’s Disease Using a Deep-Learning Algorithm to Analyze Prodromal Medical and Prescription Data
Yong-Wan Koo, Minki Kim, Woong‐Woo Lee
Journal of Clinical Neurology (2025) Vol. 21, Iss. 1, pp. 21-21
Open Access | Times Cited: 2
3D printing and artificial intelligence tools for droplet microfluidics: Advances in the generation and analysis of emulsions
Sibilla Orsini, Marco Lauricella, Andrea Montessori, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access | Times Cited: 2
Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)
Nida Aslam, Irfan Ullah Khan, Samiha Mirza, et al.
Sustainability (2022) Vol. 14, Iss. 12, pp. 7375-7375
Open Access | Times Cited: 48
Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges
Abigail Koay, Ryan K. L. Ko, Hinne Hettema, et al.
Journal of Intelligent Information Systems (2022) Vol. 60, Iss. 2, pp. 377-405
Open Access | Times Cited: 45
Leveraging explanations in interactive machine learning: An overview
Stefano Teso, Öznur Alkan, Wolfgang Stammer, et al.
Frontiers in Artificial Intelligence (2023) Vol. 6
Open Access | Times Cited: 31
Explainability and white box in drug discovery
Kevser Kübra Kırboğa, Sumra Wajid Abbasi, Ecir Uğur Küçüksille
Chemical Biology & Drug Design (2023) Vol. 102, Iss. 1, pp. 217-233
Closed Access | Times Cited: 26
Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling
Yu-Cheng Wang, Toly Chen
Expert Systems with Applications (2023) Vol. 237, pp. 121369-121369
Closed Access | Times Cited: 25
Machine learning for an explainable cost prediction of medical insurance
Ugochukwu Orji, Elochukwu Ukwandu
Machine Learning with Applications (2023) Vol. 15, pp. 100516-100516
Open Access | Times Cited: 23
Application of machine learning‐based read‐across structure‐property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye‐sensitized solar cells (DSSCs)
Souvik Pore, Arkaprava Banerjee, Kunal Roy
Molecular Informatics (2024) Vol. 43, Iss. 4
Closed Access | Times Cited: 14
Improving IoT Security With Explainable AI: Quantitative Evaluation of Explainability for IoT Botnet Detection
Rajesh Kalakoti, Hayretdin Bahşi, Sven Nõmm
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 10, pp. 18237-18254
Closed Access | Times Cited: 11
From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence
Andreas Holzinger, Janine Schweier, Christoph Gollob, et al.
Current Forestry Reports (2024) Vol. 10, Iss. 6, pp. 442-455
Open Access | Times Cited: 11
Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
Van Giao Nguyen, Prabhakar Sharma, Ümit Ağbulut, et al.
International Journal of Green Energy (2024) Vol. 21, Iss. 12, pp. 2771-2798
Closed Access | Times Cited: 8
Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI
Sheheryar Khan, Huiliang Wang, Umer Nauman, et al.
Remote Sensing (2025) Vol. 17, Iss. 1, pp. 115-115
Open Access | Times Cited: 1
On Tackling Explanation Redundancy in Decision Trees
Yacine Izza, Alexey Ignatiev, João Marques‐Silva
Journal of Artificial Intelligence Research (2022) Vol. 75, pp. 261-321
Open Access | Times Cited: 37
Logic-Based Explainability in Machine Learning
João Marques‐Silva
Lecture notes in computer science (2023), pp. 24-104
Closed Access | Times Cited: 19
Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer
Sunil Kumar, Asmita Das
Computational Biology and Chemistry (2023) Vol. 104, pp. 107867-107867
Closed Access | Times Cited: 18
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
Piotr Komorowski, Hubert Baniecki, Przemysław Biecek
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2023), pp. 3726-3732
Open Access | Times Cited: 18
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