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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, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare
Julie Gerlings, Millie Søndergaard Jensen, Arisa Shollo
Intelligent systems reference library (2021), pp. 169-198
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare
Tim Hulsen
AI (2023) Vol. 4, Iss. 3, pp. 652-666
Open Access | Times Cited: 108
Enhancing hydrogen production prediction from biomass gasification via data augmentation and explainable AI: A comparative analysis
Chiagoziem C. Ukwuoma, Dongsheng Cai, Anto Leoba Jonathan, et al.
International Journal of Hydrogen Energy (2024) Vol. 68, pp. 755-776
Closed Access | Times Cited: 17
Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review
Jinsun Jung, Hyungbok Lee, Hyunggu Jung, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16110-e16110
Open Access | Times Cited: 39
An improved explainable artificial intelligence tool in healthcare for hospital recommendation
Yu-Cheng Wang, Toly Chen, Min-Chi Chiu
Healthcare Analytics (2023) Vol. 3, pp. 100147-100147
Open Access | Times Cited: 37
The challenges of integrating explainable artificial intelligence into GeoAI
Xing Jin, Renée Sieber
Transactions in GIS (2023) Vol. 27, Iss. 3, pp. 626-645
Open Access | Times Cited: 24
An explainable transfer learning framework for multi-classification of lung diseases in chest X-rays
Aryan Nikul Patel, Ramalingam Murugan, Gautam Srivastava, et al.
Alexandria Engineering Journal (2024) Vol. 98, pp. 328-343
Open Access | Times Cited: 8
Explainable AI in Healthcare
Shantha Visalakshi Upendran
Advances in healthcare information systems and administration book series (2024), pp. 58-71
Closed Access | Times Cited: 8
The role of explainability in AI-supported medical decision-making
Anne Gerdes
Discover Artificial Intelligence (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 6
Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes
B S Darshan, Niranjana Sampathila, G. Muralidhar Bairy, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Development of AF’fective: an Explainable AI Application to Support Remote Monitoring of Atrial Fibrillation Patients After Catheter Ablation (Preprint)
Wan Jou She, Panote Siriaraya, Hibiki Iwakoshi, et al.
JMIR Human Factors (2025) Vol. 12, pp. e65923-e65923
Open Access
Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors
Eric Prince, David M. Mirsky, Todd C. Hankinson, et al.
Frontiers in Radiology (2025) Vol. 4
Open Access
Integrating eXplainable AI in Healthcare: A Web Application Framework for Advancing the One Health Paradigm
Danilo Danese, Tommaso Di Noia, Angela Lombardi, et al.
Communications in computer and information science (2025), pp. 77-88
Closed Access
Explaining Biomarker Response to Anticoagulant Therapy in Atrial Fibrillation: A Study of Warfarin and Rivaroxaban with Machine Learning Models
Adriano Veloso, Gianlucca Zuin, Laureen Sena
Lecture notes in computer science (2025), pp. 475-487
Closed Access
Integrating Ethical Principles Into the Regulation of AI-Driven Medical Software
Filzah Faheem, Mahdi Haq, Mohamed Derhab, et al.
Cureus (2025)
Open Access
Explainable AI: definition and attributes of a good explanation for health AI
Evangelia Kyrimi, Scott McLachlan, Jared M. Wohlgemut, et al.
AI and Ethics (2025)
Open Access
Spinal Metastasis—Imaging Using XAI and RAI Techniques
Arti A. Bagada, Priya Patel
(2025), pp. 115-143
Closed Access
Osteoporosis Risk Assessment and Individualized Feature Analysis Using Interpretable XAI and RAI Techniques
Shivam Rajput, Rishabha Malviya, Sathvik Belagodu Sridhar
(2025), pp. 89-113
Closed Access
Overcoming barriers in the use of artificial intelligence in point of care ultrasound
Roberto Vega, Masood Dehghan, Arun Nagdev, et al.
npj Digital Medicine (2025) Vol. 8, Iss. 1
Open Access
A Review of the Application of Generative Models in Chest X-Ray Images
欢 储
Computer Science and Application (2025) Vol. 15, Iss. 04, pp. 287-300
Closed Access
A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?
Ziming Wang, Changwu Huang, Xin Yao
ACM Transactions on Autonomous and Adaptive Systems (2024) Vol. 19, Iss. 4, pp. 1-40
Open Access | Times Cited: 3
Unveil the Black-Box Model for Healthcare Explainable AI
Rajanikanth Aluvalu, V. Sowmya Devi, Ch. Niranjan Kumar, et al.
Computational intelligence methods and applications (2024), pp. 49-70
Closed Access | Times Cited: 2
Multi-objective Feature Attribution Explanation For Explainable Machine Learning
Ziming Wang, Changwu Huang, Yun Li, et al.
ACM Transactions on Evolutionary Learning and Optimization (2023) Vol. 4, Iss. 1, pp. 1-32
Open Access | Times Cited: 6
Identifying Neuropsychiatric Disorder Subtypes and Subtype-dependent Variation in Diagnostic Deep Learning Classifier Performance
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Closed Access | Times Cited: 7
Patient information needs for transparent and trustworthy artificial intelligence in healthcare
Austin M. Stroud, Sarah A. Minteer, Xuan Zhu, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (2023), pp. 1-4
Open Access | Times Cited: 3
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