Explainable AI: The New 42?

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

Moravčík, M., Schmid, M., BURCH, N. R., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017) · Science · doi:10.1126/science.aam6960 · Cited by 885

What do we need to build explainable AI systems for the medical domain?

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

Causability and explainability of artificial intelligence in medicine

Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885

Evaluating the quality of machine learning explanations: a survey on methods and metrics

Zhou, J., Gandomi, A. H., Chen, F., & Holzinger, A. (2021) · Electronics · doi:10.3390/electronics10050593 · Cited by 606

Fairness of artificial intelligence in healthcare: review and recommendations

Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607

Notions of explainability and evaluation approaches for explainable artificial intelligence

Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598

Counterfactual explanations and how to find them: literature review and benchmarking

Guidotti, R. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00831-6 · Cited by 583

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study

Brinati, D., Campagner, A., Ferrari, D., Locatelli, M., Banfi, G., & Cabitza, F. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01597-4 · Cited by 365

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

Schwalbe, G., & Finzel, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00867-8 · Cited by 422

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Buhrmester, V., Münch, D., & Arens, M. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040048 · Cited by 403

A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022) · Applied Sciences · doi:10.3390/app12031353 · Cited by 366

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Alzubaidi, L., Al‐Amidie, M., Al‐Asadi, A., Humaidi, A. J., Al-Shamma, O., Fadhel, M. A., Zhang, J., Santamaría, J., & Duan, Y. (2021) · Cancers · doi:10.3390/cancers13071590 · Cited by 318

From Machine Learning to Explainable AI

Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306

Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Holzinger, A., Kieseberg, P., Weippl, E. R., & Tjoa, A. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99740-7_1 · Cited by 225

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204

Dermoscopy Image Analysis: Overview and Future Directions

Celebi, M. E., Codella, N., & Halpern, A. C. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2895803 · Cited by 204

Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions

Theissler, A., Spinnato, F., Schlegel, U., & Guidotti, R. (2022) · IEEE Access · doi:10.1109/access.2022.3207765 · Cited by 245

Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions

Longo, L., Goebel, R. G., Lécué, F., Kieseberg, P., & Holzinger, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_1 · Cited by 193

Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

Pisoni, G., Díaz-Rodríguez, N., Gijlers, H., & Tonolli, L. (2021) · Applied Sciences · doi:10.3390/app11020870 · Cited by 183

On the role of knowledge graphs in explainable AI

Lécué, F. (2019) · Semantic Web · doi:10.3233/sw-190374 · Cited by 179

Deep neural network for hierarchical extreme multi-label text classification

Gargiulo, F., Silvestri, S., Ciampi, M., & Pietro, G. D. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.041 · Cited by 164

Machine learning and artificial neural network accelerated computational discoveries in materials science

Hong, Y., Hou, B., Jiang, H., & Zhang, J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1450 · Cited by 160

HCI for health and wellbeing: Challenges and opportunities

Blandford, A. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.06.007 · Cited by 152

Understanding artificial intelligence ethics and safetyPreprint

Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 308

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

Piccialli, F., Cola, V. S. D., Giampaolo, F., & Cuomo, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10131-x · Cited by 153

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

Explainable Artificial Intelligence (XAI) in Biomedicine: Making AI Decisions Trustworthy for Physicians and Patients

Lötsch, J., Kringel, D., & Ultsch, A. (2021) · BioMedInformatics · doi:10.3390/biomedinformatics2010001 · Cited by 120

Digital Transformation for Sustainable Development Goals (SDGs) - A Security, Safety and Privacy Perspective on AI

Holzinger, A., Weippl, E. R., Tjoa, A. M., & Kieseberg, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84060-0_1 · Cited by 122

Explainable artificial intelligence and machine learning: A reality rooted perspective

Emmert‐Streib, F., Yli-Harja, O. P., & Dehmer, M. (2020) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1368 · Cited by 119

The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems

Nourani, M., Kabir, S., Mohseni, S., & Ragan, E. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5284 · Cited by 110

Evaluating local explanation methods on ground truth

Guidotti, R. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103428 · Cited by 99

Intelligent software engineering in the context of agile software development: A systematic literature review

Perkusich, M., Silva, L. C. E., Costa, A., Ramos, F., Saraiva, R., Freire, A., Dilorenzo, E., Dantas, E., Santos, D. F. S., Gorgônio, K., et al. (2019) · Information and Software Technology · doi:10.1016/j.infsof.2019.106241 · Cited by 92

Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges, and Future Direction

Rakholia, R., Suárez‐Cetrulo, A. L., Singh, M., & Carbajo, R. S. (2024) · IEEE Access · doi:10.1109/access.2024.3458830 · Cited by 103

To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

Amparore, E. G., Perotti, A., & Bajardi, P. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.479 · Cited by 103

Explainable AI and Multi-Modal Causability in Medicine

Holzinger, A. (2021) · i-com · doi:10.1515/icom-2020-0024 · Cited by 87

Robust Botnet DGA Detection: Blending XAI and OSINT for Cyber Threat Intelligence Sharing

Suryotrisongko, H., Musashi, Y., Tsuneda, A., & Sugitani, K. (2022) · IEEE Access · doi:10.1109/access.2022.3162588 · Cited by 90

Explainable image classification with evidence counterfactual

Vermeire, T., Brughmans, D., Goethals, S., Oliveira, R., & Martens, D. (2022) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01055-y · Cited by 102

Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session)

Felderer, M., & Ramler, R. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-65854-0_3 · Cited by 99

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira, J. J., & Monteiro, M. S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-49760-6_4 · Cited by 68

AI in learning

Niemi, H. (2021) · Journal of Pacific Rim Psychology · doi:10.1177/18344909211038105 · Cited by 105

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms

Al-Tam, R. M., Al-Hejri, A. M., Narangale, S. M., Samee, N. A., Mahmoud, N. F., Al-masni, M. A., & Al-antari, M. A. (2022) · Biomedicines · doi:10.3390/biomedicines10112971 · Cited by 74

Scenario-Based Requirements Elicitation for User-Centric Explainable AI - A Case in Fraud Detection

Cirqueira, D., Nedbal, D., Helfert, M., & Bezbradica, M. (2020) · International Cross-Domain Conference on Machine Learning and Knowledge Extraction · doi:10.1007/978-3-030-57321-8_18 · Cited by 74

Structure‐Based Drug Discovery with Deep Learning**

Özçelik, R., van Tilborg, D., Jiménez-Luna, J., & Grisoni, F. (2023) · ChemBioChem · doi:10.1002/cbic.202200776 · Cited by 71

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Sarp, S., Kuzlu, M., Wilson, E., Cali, Ü., & Güler, Ö. (2021) · Electronics · doi:10.3390/electronics10121406 · Cited by 75

The potential of genomics for infectious disease forecasting

Stockdale, J. E., Liu, P., & Colijn, C. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01233-6 · Cited by 73

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 83

“There Is Not Enough Information”: On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making

Schoeffer, J., Kuehl, N., & Machowski, Y. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533218 · Cited by 67

The Use of Responsible Artificial Intelligence Techniques in the Context of Loan Approval Processes

Purificato, E., Lorenzo, F., Fallucchi, F., & Luca, E. W. D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2081284 · Cited by 76

Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis

König, P. D., Wurster, S., & Siewert, M. Β. (2022) · Big Data & Society · doi:10.1177/20539517211069632 · Cited by 67

Catala: a programming language for the law

Merigoux, D., Chataing, N., & Protzenko, J. (2021) · Proceedings of the ACM on Programming Languages · doi:10.1145/3473582 · Cited by 56

Survey of XAI in Digital Pathology

Pocevičiūtė, M., Eilertsen, G., & Lundström, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50402-1_4 · Cited by 53

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice

Shang, Y., Tian, Y., Zhou, M., Zhou, T., Lyu, K., Wang, Z., Ran, X., Liang, T., Zhu, S., & Li, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3085003 · Cited by 48

Understanding citizen perceptions of AI in the smart city

Lehtiö, A., Hartikainen, M., Ala-Luopa, S., Olsson, T., & Väänänen, K. (2022) · AI & Society · doi:10.1007/s00146-022-01589-7 · Cited by 56

Machine Learning Introduces New Perspectives to Data Agency in K—12 Computing Education

Tedre, M., Vartiainen, H., Kahila, J., Toivonen, T., Jormanainen, I., & Valtonen, T. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9274138 · 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

Real-time traffic sign detection and classification towards real traffic scene

Wu, Y., Li, Z., Chen, Y., Nai, K., & Yuan, J. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08722-y · Cited by 46

Evaluation Metrics for XAI: A Review, Taxonomy, and Practical Applications

Kadir, M. A., Mosavi, A. H., & Sonntag, D. (2023) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines59282.2023.10297629 · Cited by 53

Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques

Murad, N. Y., Hasan, M. H., Azam, M. H., Yousuf, N., & Yalli, J. S. (2024) · IEEE Access · doi:10.1109/access.2024.3398203 · Cited by 53

The three-way-in and three-way-out framework to treat and exploit ambiguity in data

Campagner, A., Cabitza, F., & Ciucci, D. (2020) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2020.01.010 · Cited by 41

Can machine learning model with static features be fooled: an adversarial machine learning approach

Taheri, R., Javidan, R., Shojafar, M., Vinod, P., & Conti, M. (2020) · Cluster Computing · doi:10.1007/s10586-020-03083-5 · Cited by 37

“Watch out!”: Prediction-Level Intervention for Automated Driving

Wang, C., Krüger, M., & Wiebel-Herboth, C. B. (2020) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3409120.3410652 · Cited by 37

Explainable Artificial Intelligence in Data Science: From Foundational Issues Towards Socio-technical Considerations

Borrego-Díaz, J., & Galán-Páez, J. (2022) · Minds and Machines · doi:10.1007/s11023-022-09603-z · Cited by 34

Is Artificial Intelligence Ready for Standardization?

Zielke, T. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-56441-4_19 · Cited by 38

The Road to Trustworthy 6G: A Survey on Trust Anchor Technologies

Veith, B., Krummacker, D., & Schotten, H. D. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3244274 · Cited by 32

Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting

Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., & Cameli, M. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5010017 · Cited by 37

Towards Resilient Artificial Intelligence: Survey and Research Issues

Eigner, O., Eresheim, S., Kieseberg, P., Klausner, L. D., Pirker, M., Priebe, T., Tjoa, S., Marulli, F., & Mercaldo, F. (2021) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr51186.2021.9527986 · Cited by 31

From What to Why, the Growing Need for a Focus Shift Toward Explainability of AI in Digital Pathology

Border, S. P., & Sarder, P. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.821217 · Cited by 31

Augmenting Statistical Data Dissemination by Short Quantified Sentences of Natural Language

Hudec, M., Bednárová, E., & Holzinger, A. (2018) · Journal of Official Statistics · doi:10.2478/jos-2018-0048 · Cited by 29

Entropy‐based shadowed set approximation of intuitionistic fuzzy sets

Campagner, A., Dorigatti, V., & Ciucci, D. (2020) · International Journal of Intelligent Systems · doi:10.1002/int.22287 · Cited by 29

A review on AI Safety in highly automated driving

Wäschle, M., Thaler, F., Berres, A., Pölzlbauer, F., & Albers, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.952773 · Cited by 28

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Deep Learning and Image Augmentation

Rahim, S. S., Palade, V., Almakky, I., & Holzinger, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_8 · Cited by 26

A Taxonomy of Explainable Bayesian Networks

Derks, I. P., & de Waal, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-66151-9_14 · Cited by 26

Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors

Sivapalan, G., Nundy, K. K., James, A. P., Cardiff, B., & John, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3260722 · Cited by 26

Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach

Öcal, A. (2024) · IEEE Access · doi:10.1109/access.2024.3510526 · Cited by 29

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

Liu, W., Chang, C.-H., Wang, X., Liu, C., Fung, J. M., Ebrahimabadi, M., Karimi, N., Meng, X., & Basu, K. (2021) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2021.3084400 · Cited by 28

Promise of AI in DeFi, a Systematic Review

Sadman, N., Ahsan, M. M., Rahman, A., Siddique, Z., & Gupta, K. D. (2022) · Digital · doi:10.3390/digital2010006 · Cited by 25

Data and model bias in artificial intelligence for healthcare applications in New Zealand

Yogarajan, V., Dobbie, G., Leitch, S. V., Keegan, T. T., Bensemann, J., Witbrock, M., Asrani, V. M., & Reith, D. M. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1070493 · Cited by 33

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response

Vladimirov, N., & Perlman, O. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043151 · Cited by 25

Design of Automatic Strawberry Harvest Robot Suitable in Complex Environments

Huang, Z., Sklar, E. I., & Parsons, S. (2020) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3371382.3377443 · Cited by 23

What is Missing in XAI So Far?

Schmid, U., & Wrede, B. (2022) · Künstliche Intell. · doi:10.1007/s13218-022-00786-2 · Cited by 31

Hybrid reasoning in knowledge graphs: Combing symbolic reasoning and statistical reasoning

Li, W., Qi, G., & Ji, Q. (2020) · Semantic Web · doi:10.3233/sw-190375 · Cited by 25

An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming

Nadizar, G., Rovito, L., Lorenzo, A. D., Medvet, E., & Virgolin, M. (2024) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3643688 · Cited by 23

Scalable and efficient learning from crowds with Gaussian processes

Morales-Álvarez, P., Ruiz, P., Santos‐Rodríguez, R., Molina, R., & Katsaggelos, A. K. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.12.008 · Cited by 22

Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification

Li, X., & Saúde, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_4 · Cited by 22

Machine learning to identify clinically relevant Candida yeast species

Shankarnarayan, S. A., & Charlebois, D. A. (2023) · Medical Mycology · doi:10.1093/mmy/myad134 · Cited by 27

Designing environmentally efficient aquafeeds through the use of multicriteria decision support tools

Cooney, R., Wan, A. H. L., O’Donncha, F., & Clifford, E. (2021) · Current Opinion in Environmental Science & Health · doi:10.1016/j.coesh.2021.100276 · Cited by 20

Medication adherence prediction through temporal modelling in cardiovascular disease management

Hsu, W. W. Y., Warren, J., & Riddle, P. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02052-9 · Cited by 24

Moving from ‘black box’ to ‘glass box’ Artificial Intelligence in Manufacturing with XMANAI

Lampathaki, F., Agostinho, C., Glikman, Y., & Sesana, M. (2021) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc52061.2021.9570236 · Cited by 19

Advances on Data Management and Information Systems

Darmont, J., Novikov, B., Wrembel, R., & Bellatreche, L. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10235-4 · Cited by 19

Artificial Intelligence Approach in Melanoma

Curiel‐Lewandrowski, C. N., Novoa, R. A., Berry, E., Celebi, M. E., Codella, N., Giuste, F., Gutman, D. A., Halpern, A. C., Leachman, S. A., Liu, Y., et al. (2019) · Springer eBooks · doi:10.1007/978-1-4614-7322-0_43-1 · Cited by 20

Fuzzy functional dependencies and linguistic interpretations employed in knowledge discovery tasks from relational databases

Vučetić, M., Hudec, M., & Božilović, B. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103395 · Cited by 19

Insights into Learning Competence Through Probabilistic Graphical Models

Saranti, A., Taraghi, B., Ebner, M., & Holzinger, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_16 · Cited by 19

New Frontiers in Explainable AI: Understanding the GI to Interpret the GO

Cabitza, F., Campagner, A., & Ciucci, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_3 · Cited by 19

Explaining in Time

Arnold, T., Kasenberg, D., & Scheutz, M. (2021) · ACM Trans. Hum. Robot Interact. · doi:10.1145/3457183 · Cited by 12

Autonomous tissue retraction with a biomechanically informed logic based framework

Meli, D., Tagliabue, E., Dall’Alba, D., & Fiorini, P. (2021) · International Symposium on Medical Robotics · doi:10.1109/ismr48346.2021.9661573 · Cited by 18

Quantitative systems-based prediction of antimicrobial resistance evolution

Charlebois, D. A. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00304-6 · Cited by 18

Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals

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