Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer by Destie Provenzano, Jeffrey Wang, Sharad Goyal, Yuan James Rao

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High-resolution GNSS troposphere tomography through explainable deep learning-based downscaling framework by Saeid Haji-Aghajany, Saeed Izanlou, Melika Tasan, Witold Rohm, Maciej Kryza

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Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting by Chris Lochhead, Robert B. Fisher

“…There is a “black box” problem with existing machine learning models, where healthcare professionals are expected to “trust” the model making diagnoses without understanding its underlying reasoning. …”
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SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection by Putthiporn Thanathamathee, Siriporn Sawangarreerak, Siripinyo Chantamunee, Dinna Nina Mohd Nizam

“…This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. …”
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Building and explaining data-driven energy demand models for Indian states by Kieran M R Hunt, Hannah C Bloomfield

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Explainable Artificial Intelligence in Radiological Cardiovascular Imaging—A Systematic Review by Matteo Haupt, Martin H. Maurer, Rohit Philip Thomas

“…<b>Background:</b> Artificial intelligence (AI) and deep learning are increasingly applied in cardiovascular imaging. …”
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Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification by Dip Kumar Saha, Sadman Rafi, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, Md Mohsin Kabir, Nilanjan Dey

“…Finally, the popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied to the convolutional layer of the Mpox-XDE model to generate overlaid areas that effectively highlight each illness class in the dataset. …”
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Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence by Yasin Görmez, Fatma Hilal Yagin, Burak Yagin, Yalin Aygun, Hulusi Boke, Georgian Badicu, Matheus Santos De Sousa Fernandes, Abedalrhman Alkhateeb, Mahmood Basil A. Al-Rawi, Mohammadreza Aghaei, Mohammadreza Aghaei

“…ObjectivesThis study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. …”
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Deep Learning–Based Automated Imaging Classification of ADPKD by Youngwoo Kim, Seonah Bu, Cheng Tao, Kyongtae T. Bae, Theodore Steinman, Jesse Wei, Peter Czarnecki, Ivan Pedrosa, William Braun, Saul Nurko, Erick Remer, Arlene Chapman, Diego Martin, Frederic Rahbari-Oskoui, Pardeep Mittal, Vicente Torres, Marie C. Hogan, Ziad El-Zoghby, Peter Harris, James Glockner, Bernard King, Jr., Ronald Perrone, Neil Halin, Dana Miskulin, Robert Schrier, Godela Brosnahan, Berenice Gitomer, Cass Kelleher, Amirali Masoumi, Nayana Patel, Franz Winklhofer, Jared Grantham, Alan Yu, Connie Wang, Louis Wetzel, Charity G. Moore, James E. Bost, Kyongtae Bae, Kaleab Z. Abebe, J. Philip Miller, Paul A. Thompson, Josephine Briggs, Michael Flessner, Catherine M. Meyers, Robert Star, James Shayman, William Henrich, Tom Greene, Mary Leonard, Peter McCullough, Sharon Moe, Michael Rocco, David Wendler

“…Methods: We developed a deep learning–based method to automatically classify class 1 and 2 from magnetic resonance (MR) images and provide classification confidence utilizing abdominal T2-weighted MR images from 486 subjects, where transfer learning was applied. …”
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Individualized lesion-symptom mapping using explainable artificial intelligence for the cognitive impact of white matter hyperintensities by Ryanne Offenberg, Alberto De Luca, Geert Jan Biessels, Frederik Barkhof, Wiesje M. van der Flier, Argonde C. van Harten, Ewoud van der Lelij, Josien Pluim, Hugo Kuijf

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Visual explainability of 250 skin diseases viewed through the eyes of an AI‐based, self‐supervised vision transformer—A clinical perspective by Ramy Abdel Mawgoud, Christian Posch

“…Abstract Background Conventional supervised deep‐learning approaches mostly focus on a small range of skin disease images. …”
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Explainable Feature-Injected Diffusion Model for Medical Image Translation by Jung Su Ahn, Ki Hoon Kwak, Young-Rae Cho

“…It also integrates weighted heatmaps generated by explainable AI models and utilizes a cross-attention mechanism to achieve unbiased image synthesis. …”
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Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal by Zafar Iqbal, Md. Mahfuzur Rahman, Usman Mahmood, Qasim Zia, Zening Fu, Vince D. Calhoun, Sergey Plis

“…By aligning model predictions with meaningful temporal patterns in brain activity, TR bridges the gap between deep learning and clinical relevance. These findings emphasize the potential of explainable AI tools for aiding clinicians in diagnostics and treatment planning, especially in conditions characterized by disrupted temporal dynamics.…”
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TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). …”
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Biasing Rule-Based Explanations Towards User Preferences by Parisa Mahya, Johannes Fürnkranz

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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study by Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

“…ObjectiveThis study aimed to develop and evaluate a deep learning model using EMR data to predict 5-year overall survival in patients with colorectal cancer and to examine the clinical interpretability of model predictions using explainable artificial intelligence (XAI) techniques. …”
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Exploring a learning-to-rank approach to enhance the Retrieval Augmented Generation (RAG)-based electronic medical records search engines by Cheng Ye

“…Background: This study addresses the challenge of enhancing Retrieval Augmented Generation (RAG) search engines for electronic medical records (EMR) by learning users' distinct search semantics. …”
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Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex. by Pengsheng Zheng, Christos Dimitrakakis, Jochen Triesch

“…It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. …”
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A Hybrid CNN Framework DLI-Net for Acne Detection with XAI by Shaila Sharmin, Fahmid Al Farid, Md. Jihad, Shakila Rahman, Jia Uddin, Rayhan Kabir Rafi, Radia Hossan, Hezerul Abdul Karim

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QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals by Veysel Yusuf Cambay, Irem Tasci, Gulay Tasci, Rena Hajiyeva, Sengul Dogan, Turker Tuncer

“…The presented XFE model has four main phases, and these are (i) channel transformer and quadruple transition pattern (QuadTPat)-based feature generation, (ii) feature selection deploying cumulative weighted neighborhood component analysis (CWNCA), (iii) explainable results creation with DLob and (iv) classification with t algorithm-based k-nearest neighbors (tkNN) classifier. …”
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