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The Explanation and Sensitivity of AI Algorithms Supplied with Synthetic Medical Data
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/aisy.202400304openalex-snapshot
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
inferredcites →https://doi.org/10.1007/978-981-99-0601-7_51openalex-snapshot
Synthetic Time Series Data Generation Using Time GAN with Synthetic and Real-Time Data Analysis
inferredcites →https://doi.org/10.1007/s43994-024-00153-xopenalex-snapshot
Sample size calculation in biomedical, clinical and biological sciences research
inferredcites →https://doi.org/10.1016/b978-0-323-96104-2.00016-6openalex-snapshot
Advances in AI, neural networks, and brain computing: An introduction
inferredcites →https://doi.org/10.1016/j.compbiomed.2025.109834openalex-snapshot
Generative AI for synthetic data across multiple medical modalities: A systematic review of recent developments and challenges
inferredcites →https://doi.org/10.1016/j.csbj.2024.07.005openalex-snapshot
Synthetic data generation methods in healthcare: A review on open-source tools and methods
inferredcites →https://doi.org/10.1016/j.ijmedinf.2024.105763openalex-snapshot
Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications
inferredcites →https://doi.org/10.1016/j.imu.2022.100941openalex-snapshot
A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest
inferredcites →https://doi.org/10.1016/j.ins.2021.12.018openalex-snapshot
Differentially private synthetic medical data generation using convolutional GANs
inferredcites →https://doi.org/10.1016/j.knosys.2020.106622openalex-snapshot
AutoML: A survey of the state-of-the-art
inferredcites →https://doi.org/10.1016/j.media.2020.101759openalex-snapshot
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
inferredcites →https://doi.org/10.1016/j.neucom.2023.126585openalex-snapshot
ydata-profiling: Accelerating data-centric AI with high-quality data
inferredcites →https://doi.org/10.1038/s41551-021-00751-8openalex-snapshot
Synthetic data in machine learning for medicine and healthcare
inferredcites →https://doi.org/10.1038/s41598-024-74656-2openalex-snapshot
A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method
inferredcites →https://doi.org/10.1038/s41746-023-00927-3openalex-snapshot
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy
inferredcites →https://doi.org/10.1038/s42256-020-0222-1openalex-snapshot
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks
inferredcites →https://doi.org/10.1049/htl2.12039openalex-snapshot
Diabetes prediction using machine learning and explainable AI techniques
inferredcites →https://doi.org/10.1109/access.2022.3208715openalex-snapshot
Machine Learning Model Generation With Copula-Based Synthetic Dataset for Local Differentially Private Numerical Data
inferredcites →https://doi.org/10.1109/tmi.2019.2927182openalex-snapshot
Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
inferredcites →https://doi.org/10.1186/s13040-021-00244-zopenalex-snapshot
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
inferredcites →https://doi.org/10.1371/journal.pdig.0000082openalex-snapshot
Synthetic data in health care: A narrative review
inferredcites →https://doi.org/10.1515/cmb-2024-0004openalex-snapshot
Augmenting heart disease prediction with explainable AI: A study of classification models
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3390/app11052158openalex-snapshot
Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
inferredcites →https://doi.org/10.3390/diagnostics13091532openalex-snapshot
Federated Learning for Medical Image Analysis with Deep Neural Networks
inferredcites →https://doi.org/10.3390/e24020196openalex-snapshot
Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures
inferredcites →https://doi.org/10.3390/electronics11050812openalex-snapshot
Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain
inferredcites →https://doi.org/10.3390/jimaging10010008openalex-snapshot
A Robust Machine Learning Model for Diabetic Retinopathy Classification
inferredcites →https://doi.org/10.3390/jpm12081314openalex-snapshot
Framework for Testing Robustness of Machine Learning-Based Classifiers
inferredcites →https://doi.org/10.3390/s22197268openalex-snapshot
An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI
inferredcites →https://doi.org/10.3390/s23094178openalex-snapshot
A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Air Quality Prediction Using Machine Learning and Statistical Analysis: An Explainable AI Perspective
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inferred← citeshttps://doi.org/10.1016/j.ibmed.2026.100486openalex-snapshot
Healthcare bias estimation with XAI model for clinical workflow
inferred← citeshttps://doi.org/10.1101/2025.05.16.25327812openalex-snapshot
Causal Analysis of Social Support on Functional Outcomes in Stroke Rehabilitation: A Study Using Synthetic Data and Advanced Inference Methods
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9750726/v1openalex-snapshot
SY-CNN+: Synthetic-Data-Enhanced Hybrid Learning for Clinical Photograph-Based Eyelid Tumor Classification
inferred← citeshttps://doi.org/10.3390/bioengineering12070764openalex-snapshot
Implementation of a Conditional Latent Diffusion-Based Generative Model to Synthetically Create Unlabeled Histopathological Images
inferred← citeshttps://doi.org/10.3390/electronics14204050openalex-snapshot
Explainable Model of Hybrid Ensemble Learning for Prostate Cancer RNA-Seq Classification via Targeted Feature Selection
inferred← citeshttps://doi.org/10.3390/w18020214openalex-snapshot
Feasibility of Conditional Tabular Generative Adversarial Networks for Ecologically Plausible Synthetic River Water-Quality Data: A Statistical and Ecological Similarity Assessment
inferred← citeshttps://doi.org/10.37349/etat.2026.1002357openalex-snapshot
Application of explainable artificial intelligence integrating with electronic health record in oncology
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