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Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

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The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions

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A survey on the interpretability of deep learning in medical diagnosis

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From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies

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Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches

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Explainable Deep Learning Methods in Medical Image Classification: A Survey

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Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

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A survey on explainable artificial intelligence for cybersecurity

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Investigation on explainable machine learning models to predict chronic kidney diseases

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Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

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Explainable AI approaches in deep learning: Advancements, applications and challenges

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Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions

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Local interpretable model-agnostic explanation approach for medical imaging analysis: A systematic literature review

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Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson’s disease

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Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture

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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

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An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images

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A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging

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Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules

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An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm

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XAI-Based Clinical Decision Support Systems: A Systematic Review

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Explainable Artificial Intelligence: A Perspective on Drug Discovery

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Uncertainty-driven ensembles of multi-scale deep architectures for image classification

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Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images

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Interpretable AI for bio-medical applications

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A Comprehensive Study of Explainable Artificial Intelligence in Healthcare

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Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

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Applications of artificial intelligence in nuclear medicine image generation

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Current status and future directions of explainable artificial intelligence in medical imaging

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Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

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Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes

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Fostering trust and interpretability: integrating explainable AI (XAI) with machine learning for enhanced disease prediction and decision transparency

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Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

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Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

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Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME

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Skin cancer classification using explainable artificial intelligence on pre-extracted image features

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Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet–Quantum Learning

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An Explainable Classification Method of SPECT Myocardial Perfusion Images in Nuclear Cardiology Using Deep Learning and Grad-CAM

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Artificial intelligence and neuroscience: An update on fascinating relationships

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Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA

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Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare

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A Comprehensive framework for Parkinson’s disease diagnosis using explainable artificial intelligence empowered machine learning techniques

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Towards Domain-Specific Explainable AI: Model Interpretation of a Skin Image Classifier using a Human Approach

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A Scoping Review on the Progress, Applicability, and Future of Explainable Artificial Intelligence in Medicine

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Gait based Parkinson’s disease diagnosis and severity rating using multi-class support vector machine

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Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

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Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial

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Automatic detection of Parkinson’s disease from power spectral density of electroencephalography (EEG) signals using deep learning model

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Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

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Advances in Machine Learning and Deep Learning for Hormonal Disorder Diagnosis: an Exhaustive Review on PCOS, Thyroid, and Optimization Techniques

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Explainable AI for Clinical Decision Support Systems: Literature Review, Key Gaps, and Research Synthesis

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Diagnosis of Parkinson’s disease based on voice signals using SHAP and hard voting ensemble method

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Explainable machine learning with pairwise interactions for the classification of Parkinson’s disease and SWEDD from clinical and imaging features

Sarica, A., Quattrone, A., & Quattrone, A. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00688-9 · Cited by 37

Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs

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Explainable AI in action: a comparative analysis of hypertension risk factors using SHAP and LIME

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Explaining predictions and attacks in federated learning via random forests

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Assessment of Acoustic Features and Machine Learning for Parkinson’s Detection

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Advancements in deep learning and explainable artificial intelligence for enhanced medical image analysis: A comprehensive survey and future directions

Mir, A. N., & Rizvi, D. R. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111413 · Cited by 34

Brain Tumor Classification in MRI: Insights From LIME and Grad-CAM Explainable AI Techniques

Yoon, H. C., & Lin, L. P. (2025) · IEEE Access · doi:10.1109/access.2025.3603272 · Cited by 31

Advances in XAI: Explanation Interfaces in Healthcare

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A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence

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Enhanced Parkinson’s Disease Diagnosis Through Convolutional Neural Network Models Applied to SPECT DaTSCAN Images

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A Comprehensive Survey on Big Data Analytics: Characteristics, Tools and Techniques

Shahnawaz, M., & Kumar, M. (2025) · ACM Computing Surveys · doi:10.1145/3718364 · Cited by 30

The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review

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Explainable AI: To Reveal the Logic of Black-Box Models

Chinu, & Bansal, U. (2023) · New Generation Computing · doi:10.1007/s00354-022-00201-2 · Cited by 33

Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

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A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Rana, A., Dumka, A., Singh, R., Panda, M. K., & Priyadarshi, N. (2022) · Diagnostics · doi:10.3390/diagnostics12112708 · Cited by 32

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

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Hyperparameter Optimization Strategies for Tree-Based Machine Learning Models Prediction: A Comparative Study of AdaBoost, Decision Trees, and Random ForestPreprint

Mohammadagha, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5226457 · Cited by 39

Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME

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Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP

Schulte, L., Ledel, B., & Herbold, S. (2024) · Empirical Software Engineering · doi:10.1007/s10664-024-10469-1 · Cited by 28

Applications of interpretable deep learning in neuroimaging: A comprehensive review

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Explainable AI for Sensor Signal Interpretation to Revolutionize Human Health Monitoring: A Review

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Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

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A new era in colorectal cancer: Artificial Intelligence at the forefront

Farhoudian, A., Heidari, A., & Shahhosseini, R. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110926 · Cited by 27

A Survey on Explainable Anomaly Detection for Industrial Internet of Things

Huang, Z., & Wu, Y. (2022) · IEEE Conference on Dependable and Secure Computing (DSC) · doi:10.1109/dsc54232.2022.9888874 · Cited by 26

Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions

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Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System

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Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images

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