A survey on medical explainable AI (XAI): recent progress, explainability approach, human interaction and scoring system

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Tarvainen, A., & Valpola, H. (2017) · arXiv · doi:10.48550/arxiv.1703.01780 · Cited by 4,275 · see citing works

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524 · see citing works

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works

Deep learning-enabled medical computer vision

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Zhou, S. K., Greenspan, H. K., Davatzikos, C. A., Duncan, J. S., van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,292 · see citing works

Techniques for interpretable machine learning

Du, M., Liu, N., & Hu, X. (2019) · Communications of the ACM · doi:10.1145/3359786 · Cited by 1,268 · see citing works

A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature · doi:10.1038/s41586-019-1390-1 · Cited by 1,086 · see citing works

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Frangi, A. F., Schnabel, J. A., Davatzikos, C. A., Alberola-López, C., & Fichtinger, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3 · Cited by 1,049 · see citing works

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047 · see citing works

A Survey on Neural Network Interpretability

Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923 · see citing works

Interpolation Consistency Training for Semi-supervised Learning

Verma, V., Kawaguchi, K., Lamb, A., Kannala, J., Solin, A., Bengio, Y., & López-Paz, D. (2019) · Neural Networks · doi:10.1016/j.neunet.2021.10.008 · Cited by 895 · see citing works

Transfusion: Understanding Transfer Learning for Medical ImagingPreprint

Raghu, M., Zhang, C., Kleinberg, J. M., & Samy Bengio, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07208 · Cited by 845 · see citing works

Effect of Piperacillin-Tazobactam vs Meropenem on 30-Day Mortality for Patients With E coli or Klebsiella pneumoniae Bloodstream Infection and Ceftriaxone Resistance

Harris, P. N. A., Tambyah, P. A., Lye, D. C., Mo, Y., Lee, T. H., Yılmaz, M., Alenazi, T. H., Arabi, Y. M., Falcone, M., Bassetti, M., et al. (2018) · JAMA · doi:10.1001/jama.2018.12163 · Cited by 789 · see citing works

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785 · see citing works

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766 · see citing works

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. (2019) · RePEc: Research Papers in Economics · Cited by 580 · see citing works

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database

van Ginneken, B., Stegmann, M. B., & Loog, M. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.02.002 · Cited by 540 · see citing works

Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives

Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507 · see citing works

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

Payrovnaziri, S. N., Chen, Z., Rengifo‐Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa053 · Cited by 318 · see citing works

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

Nazar, M., Alam, M. M., Yafi, E., & Su'ud, M. M. (2021) · IEEE Access · doi:10.1109/access.2021.3127881 · Cited by 312 · see citing works

Interpretable Basis Decomposition for Visual Explanation

Zhou, B., Sun, Y., Bau, D., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_8 · Cited by 270 · see citing works

"What is relevant in a text document?": An interpretable machine learning approach

Arras, L., Horn, F., Montavon, G., Müller, K., & Samek, W. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181142 · Cited by 268 · see citing works

Interpretable Deep Models for ICU Outcome Prediction.

Che, Z., Purushotham, S., Khemani, R. G., & Liu, Y. (2016) · PubMed Central · Cited by 240 · see citing works

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie, S., Yu, Z., & Lv, Z. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.016728 · Cited by 240 · see citing works

Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Tang, Z., Chuang, K. V., DeCarli, C. S., Jin, L., Beckett, L. A., Keiser, M. J., & Dugger, B. N. (2019) · Nature Communications · doi:10.1038/s41467-019-10212-1 · Cited by 228 · see citing works

A Survey of Data-driven and Knowledge-aware eXplainable AI

Li, X.-H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2983930 · Cited by 224 · see citing works

Learning representations for the early detection of sepsis with deep neural networks

Kam, H. J., & Kim, H. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.015 · Cited by 218 · see citing works

Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis

Delahanty, R. J., Alvarez, J., Flynn, L. M., Sherwin, R. L., & Jones, S. S. (2019) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2018.11.036 · Cited by 206 · see citing works

An attention based deep learning model of clinical events in the intensive care unit

Kaji, D. A., Zech, J. R., Kim, J., Cho, S. K.-W., Dangayach, N. S., Costa, A. B., & Oermann, E. K. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211057 · Cited by 199 · see citing works

Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

Lee, H., Yoon, S. B., Yang, S. M., Kim, W. H., Ryu, H. G., Jung, C.-W., Suh, K., & Lee, K. H. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7110428 · Cited by 197 · see citing works

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Kong, G., Lin, K., & Hu, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01271-2 · Cited by 180 · see citing works

DeepSOFA: a continuous acuity score for critically ill patients using clinically interpretable deep learning.

Shickel, B., Loftus, T. J., Adhikari, L., Ozrazgat-Baslanti, T., Bihorac, A., & Rashidi, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-38491-0 · Cited by 165 · see citing works

A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

Carrington, A., Fieguth, P., Qazi, H. A., Holzinger, A., Chen, H., Mayr, F., & Manuel, D. G. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1014-6 · Cited by 157 · see citing works

Explainable AI for Healthcare: From Black Box to Interpretable Models

Adadi, A., & Berrada, M. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-15-0947-6_31 · Cited by 137 · see citing works

Interpretable heartbeat classification using local model-agnostic explanations on ECGs

Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., Cabitza, F., & Gamboa, H. F. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104393 · Cited by 118 · see citing works

Interpretable machine learning prediction of all-cause mortality

Qiu, W., Chen, H., Dincer, A. B., Lundberg, S., Kaeberlein, M. R., & Lee, S. (2022) · Communications Medicine · doi:10.1038/s43856-022-00180-x · Cited by 112 · see citing works

SurvLIME: A method for explaining machine learning survival models

Kovalev, M. S., Utkin, L. V., & Kasimov, E. M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106164 · Cited by 108 · see citing works

Using a machine learning approach to predict mortality in critically ill influenza patients: A cross-sectional retrospective multicentre study in Taiwan

Hu, C.-A., Chen, C.-M., Fang, Y.-C., Liang, S.-J., Wang, H.-C., Fang, W.-F., Sheu, C.-C., Perng, W.-C., Yang, K.-Y., Kao, K.-C., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-033898 · Cited by 106 · see citing works

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Ye, J., Yao, L., Shen, J., Janarthanam, R., & Luo, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01318-4 · Cited by 105 · see citing works

The Ten Commandments of Ethical Medical AI

Muller, H., Mayrhofer, M. T., Van Veen, E.-B., & Holzinger, A. (2021) · Computer · doi:10.1109/mc.2021.3074263 · Cited by 102 · see citing works

Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier

Davoodi, R., & Moradi, M. H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.02.008 · Cited by 100 · see citing works

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

Theis, J., Galanter, W., Boyd, A. D., & Darabi, H. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3092969 · Cited by 100 · see citing works

Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia

Oonsivilai, M., Mo, Y., Luangasanatip, N., Lubell, Y., Miliya, T., Tan, P., Loeuk, L., Turner, P., & Cooper, B. S. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14847.1 · Cited by 95 · see citing works

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

Thomas, A. W., Heekeren, H. R., Müller, K., & Samek, W. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01321 · Cited by 93 · see citing works

Ensemble Image Explainable AI (XAI) Algorithm for Severe Community-Acquired Pneumonia and COVID-19 Respiratory Infections

Zou, L., Goh, H. L., Liew, C. J. Y., Quah, J. L., Gu, G. T., Chew, J. J., Kumar, M. P., Ang, C. G. L., & Ta, A. W. A. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3153754 · Cited by 93 · see citing works

Responsible artificial intelligence in healthcare: predicting and preventing insurance claim denials for economic and social wellbeing

Johnson, M., Albizri, A., & Harfouche, A. (2023) · Information Systems Frontiers · doi:10.1007/s10796-021-10137-5 · Cited by 88 · see citing works

The European Legal Framework for Medical AI

Schneeberger, D., Stöger, K., & Holzinger, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-57321-8_12 · Cited by 85 · see citing works

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

Ferreira, J. J., & Monteiro, M. D. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49760-6_4 · Cited by 83 · see citing works

Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis

Taneja, I., Reddy, B., Damhorst, G. L., Zhao, S. D., Hassan, U., Price, Z., Jensen, T., Ghonge, T., Patel, M. J., Wachspress, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-09766-1 · Cited by 76 · see citing works

Antibiotic Therapy for Pseudomonas aeruginosa Bloodstream Infections: How Long Is Long Enough?

Fabre, V., Amoah, J., Cosgrove, S. E., & Tamma, P. D. (2019) · Clinical Infectious Diseases · doi:10.1093/cid/ciz223 · Cited by 75 · see citing works

Building and Interpreting Deep Similarity Models

Eberle, O., Büttner, J., Kräutli, F., Müller, K., Valleriani, M., & Montavon, G. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3020738 · Cited by 68 · see citing works

Catheter removal and outcomes of multidrug-resistant central-line-associated bloodstream infection

Burnham, J. P., Rojek, R. P., & Kollef, M. H. (2018) · Medicine · doi:10.1097/md.0000000000012782 · Cited by 67 · see citing works

Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction

Le, S., Allen, A. O., Calvert, J. S., Palevsky, P. M., Braden, G. L., Patel, S., Pellegrini, E., Green‐Saxena, A., Hoffman, J. L., & Das, R. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2021.02.031 · Cited by 67 · see citing works

Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations

Zhao, G., Zhou, B., Wang, K., Jiang, R., & Xu, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_55 · Cited by 57 · see citing works

Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation

Kim, K., Yang, H., Yi, J., Son, H. E., Ryu, J. Y., Kim, Y. C., Jeong, J. C., Chin, H. J., Na, K. Y., Chae, D., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/24120 · Cited by 57 · see citing works

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 · see citing works

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis

García-Gallo, J. E., Fonseca-Ruiz, N. J., Celi, L. A., & Duitama, F. (2018) · Medicina Intensiva · doi:10.1016/j.medin.2018.07.016 · Cited by 56 · see citing works

Machine Learning Algorithms for Predicting and Risk Profiling of Cardiac Surgery-Associated Acute Kidney Injury

Penny‐Dimri, J. C., Bergmeir, C., Reid, C. M., Williams‐Spence, J., Cochrane, A. D., & Smith, J. A. (2020) · Seminars in Thoracic and Cardiovascular Surgery · doi:10.1053/j.semtcvs.2020.09.028 · Cited by 56 · see citing works

Systematic review and subgroup analysis of the incidence of acute kidney injury (AKI) in patients with COVID-19

Xu, Z., Tang, Y., Huang, Q., Fu, S., Li, X., Lin, B., Xu, A., & Chen, J. (2021) · BMC Nephrology · doi:10.1186/s12882-021-02244-x · Cited by 56 · see citing works

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study

Hsu, C., Liu, C., Tain, Y., Kuo, C.-Y., & Lin, Y.-C. (2020) · Journal of Medical Internet Research · doi:10.2196/16903 · Cited by 55 · see citing works

Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients

Jentzer, J. C., Kashou, A. H., Attia, Z. I., López-Jiménez, F. A., Kapa, S., Friedman, P. A., & Noseworthy, P. A. (2020) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.10.074 · Cited by 54 · see citing works

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

Houben, S., Abrecht, S., Akila, M., Bär, A., Brockherde, F., Feifel, P., Fingscheidt, T., Gannamaneni, S. S., Ghobadi, S. E., Hammam, A., et al. (2022) · Springer eBooks · doi:10.1007/978-3-031-01233-4_1 · Cited by 52 · see citing works

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

Hussain, S. M., Buongiorno, D., Altini, N., Berloco, F., Prencipe, B., Moschetta, M., Bevilacqua, V., & Brunetti, A. (2022) · Applied Sciences · doi:10.3390/app12126230 · Cited by 52 · see citing works

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

Hou, J., & Gao, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-95680-6 · Cited by 50 · see citing works

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

Alfieri, F., Ancona, A., Tripepi, G. L., CROSETTO, D. B., Randazzo, V., Paviglianiti, A., Pasero, E. G. A., Vecchi, L., Cauda, V., & Fagugli, R. M. (2021) · Journal of Nephrology · doi:10.1007/s40620-021-01046-6 · Cited by 48 · see citing works

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Qu, C., Gao, L., Yu, X., Wei, M., Fang, G.-Q., He, J., Cao, L., Ke, L., Tong, Z., Li, W., et al. (2020) · Gastroenterology Research and Practice · doi:10.1155/2020/3431290 · Cited by 47 · see citing works

Developing an ensemble machine learning model for early prediction of sepsis-associated acute kidney injury

Zhang, L., Wang, Z., Zhou, Z., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104932 · Cited by 47 · see citing works

Comparative Effectiveness of Exclusive Exposure to Nafcillin or Oxacillin, Cefazolin, Piperacillin/Tazobactam, and Fluoroquinolones Among a National Cohort of Veterans With Methicillin-Susceptible Staphylococcus aureus Bloodstream Infection

Beganovic, M., Cusumano, J. A., Lopes, V., et al. (2019) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofz270 · Cited by 41 · see citing works

Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

Nie, X., Cai, Y., Liu, J., Liu, X., Zhao, J., Yang, Z., Wen, M., & Liu, L. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2020.610531 · Cited by 40 · see citing works

Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

He, Z., Zhou, J., Liu, Z., Dong, S., Zhang, Y., Shen, T. H., Zheng, S., & Xu, X. (2021) · Hepatobiliary & pancreatic diseases international · doi:10.1016/j.hbpd.2021.02.001 · Cited by 39 · see citing works

Predictors of Mortality in Critically Ill COVID‐19 Patients Demanding High Oxygen Flow: A Thin Line between Inflammation, Cytokine Storm, and Coagulopathy

Popadić, V., Klašnja, S., Milic, N. M., Rajović, N., Aleksic, A., Milenkovic, M., Crnokrak, B., Balint, B., Todorović, M., Mrda, D., et al. (2021) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2021/6648199 · Cited by 39 · see citing works

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists

Rueckel, J., Kunz, W. G., Hoppe, B. F., Patzig, M., Notohamiprodjo, M., Meinel, F. G., Cyran, C. C., Ingrisch, M., Ricke, J., & Sabel, B. O. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004397 · Cited by 37 · see citing works

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies

Napolitano, F., Xu, X., & Gao, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab456 · Cited by 37 · see citing works

A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI)

Yang, Y., Mei, G., & Piccialli, F. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3190265 · Cited by 36 · see citing works

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

He, L. N., Zhang, Q., Li, Z., Shen, L., Zhang, J., Wang, P., Wu, S., Zhou, T., Xu, Q., Chen, X., et al. (2020) · Kidney Diseases · doi:10.1159/000511403 · Cited by 34 · see citing works

Explanation Perspectives from the Cognitive Sciences---A Survey

Srinivasan, R., & Chander, A. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/670 · Cited by 33 · see citing works

GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal SupervisionPreprint

Avilés-Rivero, A. I., Papadakis, N., Li, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10085 · Cited by 33 · see citing works

Effect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Data

Schallner, L., Rabold, J., Scholz, O., & Schmid, U. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43823-4_13 · Cited by 31 · see citing works

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Mamandipoor, B., Frutos–Vivar, F., Peñuelas, Ó., Rezar, R., Raymondos, K., Muriel, A., Du, B., Thille, A. W., Ríos, F., Gonzalez, M. A., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01506-w · Cited by 26 · see citing works

Is a single set of negative blood cultures sufficient to ensure clearance of bloodstream infection in patients with Staphylococcus aureus bacteremia? The skip phenomenon

Fiala, J. A., Palraj, R., Sohail, M. R., et al. (2019) · Infection · doi:10.1007/s15010-019-01339-w · Cited by 21 · see citing works

Research on Explainable Artificial Intelligence Techniques: An User Perspective

Morais, F. L. D., Cinalli, D., & García, A. C. B. (2021) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437820 · Cited by 16 · see citing works

Explain Black-box Image Classifications Using Superpixel-based Interpretation

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Local Interpretable Classifier Explanations with Self-generated Semantic Features

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Detection of Drug–Drug and Drug–Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests

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A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS

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Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy OutbreakPreprint

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A Review of Explainable Artificial Intelligence in Healthcare

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Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

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Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

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Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review

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The Apply Of Constructive Method In Mathematics

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On Constructive Groups

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As a Psychotherapist, How Do I Recognize Constructive and Destructive Use of Power?

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Constructive Social Work: Towards a New Practice

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