On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Survey and critique of techniques for extracting rules from trained artificial neural networks

Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168

A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges

Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212

Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making

Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2022.103839 · Cited by 161

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156

Ethical Dimensions of Visualization Research

Correll, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300418 · Cited by 150

Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection

Adler, A. I., & Painsky, A. (2022) · Entropy · doi:10.3390/e24050687 · Cited by 149

Explaining the Explainer: A First Theoretical Analysis of LIME

Garreau, D., & von Luxburg, U. (2020) · arXiv · doi:10.48550/arxiv.2001.03447 · Cited by 143

GLocalX—From Local to Global Explanations of Black Box AI Models

Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103457 · Cited by 140

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141

A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data

Rashed-Al-Mahfuz, M., Moni, M. A., Uddin, S., Alyami, S. A., Summers, M. A., & Eapen, V. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3050925 · Cited by 138

Interpretable machine learning for weather and climate prediction: A review

Yang, R., Hu, J., Li, Z., Mu, J., Yu, T., Xia, J., Li, X., Dasgupta, A., & Xiong, H. (2024) · Atmospheric Environment · doi:10.1016/j.atmosenv.2024.120797 · Cited by 139

Artificial intelligence explainability: the technical and ethical dimensions

McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2020.0363 · Cited by 136

Four Principles of Explainable Artificial Intelligence

Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020) · doi:10.6028/nist.ir.8312-draft · Cited by 136

Explainable Artificial Intelligence for Tabular Data: A Survey

Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135

XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010006 · Cited by 131

Falling into the Echo Chamber: The Italian Vaccination Debate on Twitter

Cossard, A., De Francisci Morales, G., Kalimeri, K., Mejova, Y., Paolotti, D., & Starnini, M. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7285 · Cited by 126

Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives

Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024) · WIREs Climate Change · doi:10.1002/wcc.914 · Cited by 130

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

Liao, Q. V., & Varshney, K. R. (2021) · arXiv · doi:10.48550/arxiv.2110.10790 · Cited by 120

A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing)

Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118

Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Rojat, T., Puget, R., Filliat, D., Del Ser, J., Gelin, R., & Díaz-Rodríguez, N. (2021) · arXiv · doi:10.48550/arxiv.2104.00950 · Cited by 116

DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

Han, D., Wang, Z., Chen, W., Zhong, Y., Wang, S., Zhang, H., Yang, J., Shi, X., & Yin, X. (2021) · Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3460120.3484589 · Cited by 121

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Shahriar, S., Allana, S., Hazratifard, S. M., & Dara, R. (2023) · IEEE Access · doi:10.1109/access.2023.3287195 · Cited by 114

How can I choose an explainer?: An Application-grounded Evaluation of Post-hoc Explanations

Jesus, S., Belém, C., Balayan, V., Bento, J., Saleiro, P., Bizarro, P., & Gama, J. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445941 · Cited by 112

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood

Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2022) · Psychological Medicine · doi:10.1017/s0033291720002718 · Cited by 108

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Quanyang, W., Yao, H., Sicong, W., Linlin, Q., Zewei, Z., Donghui, H., Hongjia, L., & Shijun, Z. (2024) · Cancer Medicine · doi:10.1002/cam4.7140 · Cited by 111

Designing a feature selection method based on explainable artificial intelligence

Zacharias, J., von Zahn, M., Chen, J., & Hinz, O. (2022) · Electronic Markets · doi:10.1007/s12525-022-00608-1 · Cited by 109

Towards Better Explanations of Class Activation Mapping

Jung, H., & Oh, Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00137 · Cited by 112

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Ghaemmaghami, H., & Fookes, C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3027910 · Cited by 105

XGBoost model as an efficient machine learning approach for PFAS removal: effects of material characteristics and operation conditions

Karbassiyazdi, E., Fattahi, F., Yousefi, N., Tahmassebi, A., Taromi, A. A., Manzari, J. Z., Gandomi, A. H., Altaee, A., & Razmjou, A. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114286 · Cited by 104

Survey on Explainable AI: Techniques, challenges and open issues

Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Moazemi, S., Vahdati, S., Li, J., Kalkhoff, S., Castano, L. J. V., Dewitz, B., Bibo, R., Sabouniaghdam, P., Tootooni, M. S., Bundschuh, R. A., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1109411 · Cited by 97

COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations

Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96

Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis

Tsai, Y.-H. H., Ma, M., Yang, M., Salakhutdinov, R., & Morency, L.-P. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.143 · Cited by 98

Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments

Jacobson, N. C., & Bhattacharya, S. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104013 · Cited by 92

Explainable Artificial Intelligence: a Systematic Review

Vilone, G., & Longo, L. (2020) · arXiv · doi:10.48550/arxiv.2006.00093 · Cited by 269

Regulatory Perspectives for AI/ML Implementation in Pharmaceutical GMP Environments

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18060901 · Cited by 90

Debugging Tests for Model Explanations

Adebayo, J., Muelly, M., Liccardi, I., & Kim, B. (2020) · arXiv · doi:10.48550/arxiv.2011.05429 · Cited by 88

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

Nam, W.-J., Gur, S., Choi, J., Wolf, L., & Lee, S.-W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5632 · Cited by 98

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

Ladbury, C., Zarinshenas, R., Semwal, H., Tam, A., Vaidehi, N., Rodin, A. S., Liu, A., Glaser, S., Salgia, R., & Amini, A. (2022) · Translational Cancer Research · doi:10.21037/tcr-22-1626 · Cited by 86

FairLens: Auditing black-box clinical decision support systems

Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86

AI and machine learning ethics, law, diversity, and global impact

Drabiak, K., Kyzer, S., Nemov, V., & El Naqa, I. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20220934 · Cited by 84

Counterfactual Explanations for Multivariate Time Series

Ates, E., Aksar, B., Leung, V. J., & Coskun, A. K. (2021) · 2021 International Conference on Applied Artificial Intelligence (ICAPAI) · doi:10.1109/icapai49758.2021.9462056 · Cited by 88

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

Li, Z., Cong, Y., Chen, X., Qi, J., Sun, J., Yan, T., Yang, H., Liu, J., Lu, E., Wang, L., et al. (2023) · iScience · doi:10.1016/j.isci.2022.105872 · Cited by 82

Enhancing scientific discoveries in molecular biology with deep generative models

Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82

Explainable AI for COVID-19 CT Classifiers: An Initial Comparison Study

Ye, Q., Xia, J., & Yang, G. (2021) · 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms52027.2021.00103 · Cited by 83

An Explainable AI framework for credit evaluation and analysis

Nallakaruppan, M. K., Balusamy, B., Shri, M. L., Malathi, V., & Bhattacharyya, S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111307 · Cited by 83

Copy number variation in plasma as a tool for lung cancer prediction using Extreme Gradient Boosting (XGBoost) classifier

Yu, D., Liu, Z., Su, C., Han, Y., Duan, X., Zhang, R., Liu, X., Yang, Y., & Xu, S. (2020) · Thoracic Cancer · doi:10.1111/1759-7714.13204 · Cited by 80

Psychological foundations of explainability and interpretability in artificial intelligence

Broniatowski, D. A. (2021) · doi:10.6028/nist.ir.8367 · Cited by 76

Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring

Mehdiyev, N., & Fettke, P. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-64949-4_1 · Cited by 78

Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making

Lee, M. H., & Chew, C. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610218 · Cited by 78

On the Detection of Disinformation Campaign Activity with Network Analysis

Vargas, L., Emami, P., & Traynor, P. (2020) · Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop · doi:10.1145/3411495.3421363 · Cited by 74

Explainable and Robust Artificial Intelligence for Trustworthy Resource Management in 6G Networks

Khan, N., Coleri, S., Abdallah, A., Celik, A., & Eltawil, A. M. (2024) · IEEE Communications Magazine · doi:10.1109/mcom.001.2300172 · Cited by 74

XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction

Zhang, Y., Xu, F., Zou, J., Petrosian, O. L., & Krinkin, K. V. (2021) · 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) · doi:10.1109/neuront53022.2021.9472817 · Cited by 76

Explainable Activity Recognition for Smart Home Systems

Das, D., Nishimura, Y., Vivek, R. P., Takeda, N., Fish, S. T., Plötz, T., & Chernova, S. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3561533 · Cited by 70

Explainable machine learning for public policy: Use cases, gaps, and research directions

Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73

The rationality of explanation or human capacity? Understanding the impact of explainable artificial intelligence on human-AI trust and decision performance

Wang, P., & Ding, H. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103732 · Cited by 68

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

Zhu, B., Shin, U., & Shoaran, M. (2021) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2021.3112756 · Cited by 67

Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model

Pereira, F. D., Fonseca, S. C., Oliveira, E. H. T., Cristea, A. I., Bellhauser, H., Rodrigues, L., Oliveira, D. B. F., Isotani, S., & Carvalho, L. S. G. (2021) · IEEE Access · doi:10.1109/access.2021.3105956 · Cited by 63

Explainable AI In Education : Current Trends, Challenges, And Opportunities

Rachha, A., & Seyam, M. (2023) · SoutheastCon 2023 · doi:10.1109/southeastcon51012.2023.10115140 · Cited by 68

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure

Chiang, P.-H., Wong, M., & Dey, S. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3098173 · Cited by 64

Sibyl: Understanding and addressing the usability challenges of machine learning in high-stakes decision making

Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114864 · Cited by 62

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina, I., & Simoff, S. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.660206 · Cited by 60

Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

Beaudouin, V., Bloch, I., Bounie, D., Clémençon, S., d'Alché-Buc, F., Eagan, J., Maxwell, W., Mozharovskyi, P., & Parekh, J. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3559477 · Cited by 61

Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity

Carton, S., Mei, Q., & Resnick, P. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7282 · Cited by 58

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Aussó, S., Alberich, L. C., Marias, K., Tsiknakis, M., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.09658 · Cited by 58

Behavioral Use Licensing for Responsible AI

Contractor, D., McDuff, D., Haines, J. K., Lee, J., Hines, C., Hecht, B., Vincent, N., & Li, H. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533143 · Cited by 54

Application of interpretable machine learning for early prediction of prognosis in acute kidney injury

Hu, C., Tan, Q., Zhang, Q., Li, Y., Wang, F., Zou, X., & Peng, Z. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.003 · Cited by 57

Decoding the Black Box: Explainable AI (XAI) for Cancer Diagnosis, Prognosis, and Treatment Planning—A State-of-the-Art Systematic Review

Abas Mohamed, Y., Ee Khoo, B., Shahrimie Mohd Asaari, M., Ezane Aziz, M., & Rahiman Ghazali, F. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105689 · Cited by 57

Farsight: Fostering Responsible AI Awareness During AI Application Prototyping

Wang, Z. J., Kulkarni, C., Wilcox, L., Terry, M., & Madaio, M. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642335 · Cited by 55

Multimodal risk prediction with physiological signals, medical images and clinical notes

Wang, Y., Yin, C., & Zhang, P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26772 · Cited by 66

Explaining Neural Networks Semantically and Quantitatively

Chen, R., Chen, H., Huang, G., Ren, J., & Zhang, Q. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00928 · Cited by 56

Establishing and evaluating trustworthy AI: overview and research challenges

Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59

Fairwashing: the risk of rationalization

Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., & Tapp, A. (2019) · arXiv · doi:10.48550/arxiv.1901.09749 · Cited by 54

A systematic review of current AI techniques used in the context of the SDGs

Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-024-00668-5 · Cited by 55

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

Deng, S., Zhu, Y., Yu, Y., & Huang, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121710 · Cited by 52

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

He, S., Li, X., DelSole, T., Ravikumar, P., & Banerjee, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16090 · Cited by 51

Integrating machine learning predictions for perioperative risk management: towards an empirical design of a flexible-standardized risk assessment tool

Abraham, J., Bartek, B., Meng, A., Ryan King, C., Xue, B., Lu, C., & Avidan, M. S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104270 · Cited by 50

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository · doi:10.17863/cam.69929 · Cited by 49

Principles and Practice of Explainable Machine Learning

Belle, V., & Papantonis, I. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.688969 · Cited by 99

Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery

Wagner, P., Mehari, T., Haverkamp, W., & Strodthoff, N. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108525 · Cited by 48

Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate biopsy

Suh, J., Yoo, S., Park, J., Cho, S. Y., Cho, M. C., Son, H., & Jeong, H. (2020) · BJU International · doi:10.1111/bju.15122 · Cited by 45

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data

Lehmann, V., Föll, S., Maritsch, M., van Weenen, E., Kraus, M., Lagger, S., Odermatt, K., Albrecht, C., Fleisch, E., Zueger, T., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2290 · Cited by 53

Detecting and unmasking AI-generated texts through explainable artificial intelligence using stylistic features

Shah, A., Ranka, P., Dedhia, U., Prasad, S., Muni, S., & Bhowmick, K. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01410110 · Cited by 47

The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations

Du, Y., Antoniadi, A. M., McNestry, C., McAuliffe, F. M., & Mooney, C. (2022) · Applied Sciences · doi:10.3390/app122010323 · Cited by 47

Explainability Methods for Identifying Root-Cause of SLA Violation Prediction in 5G Network

Terra, A., Inam, R., Baskaran, S., Batista, P., Burdick, I., & Fersman, E. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322496 · Cited by 50

Explainable Machine Learning to Predict Successful Weaning Among Patients Requiring Prolonged Mechanical Ventilation: A Retrospective Cohort Study in Central Taiwan

Lin, M.-Y., Li, C.-C., Lin, P.-H., Wang, J.-L., Chan, M.-C., Wu, C.-L., & Chao, W.-C. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.663739 · Cited by 45

A review of explainable AI techniques and their evaluation in mammography for breast cancer screening

Shifa, N., Saleh, M., Akbari, Y., & Al Maadeed, S. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110492 · Cited by 45

A machine learning model for screening thermodynamic stable lead-free halide double perovskites

Liang, G.-Q., & Zhang, J. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2021.111172 · Cited by 44

Fast TreeSHAP: Accelerating SHAP value computation for trees

Yang, J. (2021) · arXiv · doi:10.48550/arxiv.2109.09847 · Cited by 44

Current State and Prospects of Artificial Intelligence in Allergy

van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Custovic, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 43

Explaining Any Time Series Classifier

Guidotti, R., Monreale, A., Spinnato, F., Pedreschi, D., & Giannotti, F. (2020) · 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi50398.2020.00029 · Cited by 46

A systematic review of explainable artificial intelligence in urban building energy modeling: methods, applications, and future directions

Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42

Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment

Cho, H. J., Shu, M., Bekiranov, S., Zang, C., & Zhang, A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad113 · Cited by 42

From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks

Alfeo, A. L., Zippo, A. G., Catrambone, V., Cimino, M. G. C. A., Toschi, N., & Valenza, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107550 · Cited by 38

Machine Learning Explainability for External Stakeholders

Bhatt, U., Andrus, M., Weller, A., & Xiang, A. (2020) · arXiv · doi:10.48550/arxiv.2007.05408 · Cited by 41

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