Description
This book constitutes the proceedings of the 4th International Workshop on Explainable Artificial Intelligence in Healthcare, XAI-Healthcare 2025, and the Second International Workshop on Process Mining Applications for Healthcare, PM4H 2025, held in Pavia, Italy, during June 26, 2025.
The 14 full papers were included in this were carefully reviewed and selected from 27 submissions. They focus on Machine Learning, Decision Support Systems, Natural Language, Human-Computer Interaction, and Healthcare sciences.
.- International Workshop on Explainable Artificial Intelligence in Healthcare. .- Enabling Visual and Textual Explanation in Diagnostics: A Federated Learning Approach with Medical Vision-Language Models..- A User-Centric Analysis of Explainability in AI-Based Medical Image Diagnosis..- Spot the Relevance: Evaluating Pixel Attribution Methods for Clinical Significance in Radiological Imaging..- Evaluating LIME and SHAP under Class Imbalance: A Lung Cancer Prediction Case Study using CPRD data..- Explainable Artificial Intelligence in Speech-Based Cognitive Decline Detection: A Systematic Review..- Cancer Care Needs Explainable Artificial Intelligence: Motivations from Potential Users..- Data-centric explanation methods for healthcare professionals. .- International Workshop on Process Mining applications for Healthcare. .- Predictive Process Monitoring for the Next Activities in Clinical Pathways..- Towards Automated Compliance Checking for Care Trajectories: Process Extraction Using Large Language Models..- Predicting Next Clinical Event in Amyotrophic Lateral Sclerosis using Process-Oriented Machine Learning Models: a Case Study..- A Novel Way to Evaluate Medical Discharge Predictions, a research paper..- Transforming Event Logs into Narrative Texts for Outcome Prediction: A Case Study in a Hospital Emergency Department..- Federated I-PALIA: Privacy-By-Design Distributed Process Discovery for Duplicated Activities in Healthcare..- From XAI-Driven Decision Paths to Processes: Mining and Clustering Decision Paths for Interpretable Kidney Transplant Prediction.
