{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T13:04:35Z","timestamp":1767791075643,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Intracranial Hemorrhage (ICH) remains a critical life-threatening condition where timely and accurate diagnosis using non-contrast Computed Tomography (CT) scans is vital to reduce mortality and long-term disability. Deep learning methods have shown strong potential for automated hemorrhage detection, yet most existing approaches lack confidence quantification and clinical interpretability, which limits their adoption in high-stakes care. This study presents X-HEM, an explainable hemorrhage ensemble model for reliable detection of Intracranial Hemorrhage (ICH) on non-contrast head CT scans. The aim is to improve diagnostic accuracy, interpretability, and confidence for real-time clinical decision support. X-HEM integrates three convolutional backbones (VGG16, ResNet50, DenseNet121) through soft voting. Bayesian uncertainty is estimated using Monte Carlo Dropout, while Grad-CAM++ and SHAP provide spatial and global interpretability. Training and validation were conducted on the RSNA ICH dataset, with external testing on CQ500. The model achieved AUCs of 0.96 (RSNA) and 0.94 (CQ500), demonstrated well-calibrated confidence (low Brier\/ECE), and provided explanations that aligned with radiologist-marked regions. The integration of ensemble learning, Bayesian uncertainty, and dual explainability enables X-HEM to deliver confidence-aware, interpretable ICH predictions suitable for clinical use.<\/jats:p>","DOI":"10.3390\/computers15010033","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:26:56Z","timestamp":1767774416000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["X-HEM: An Explainable and Trustworthy AI-Based Framework for Intelligent Healthcare Diagnostics"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-3715","authenticated-orcid":false,"given":"Mohammad F.","family":"Al-Hammouri","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9111-0990","authenticated-orcid":false,"given":"Bandi","family":"Vamsi","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Madanapalle Institute of Technology & Science, Deemed to be University, Madanapalle 517325, Andhra Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0392-6955","authenticated-orcid":false,"given":"Islam T.","family":"Almalkawi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4576-2781","authenticated-orcid":false,"given":"Ali","family":"Al Bataineh","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Center, Norwich University, Northfield, VT 05663, USA"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e18850","DOI":"10.7717\/peerj.18850","article-title":"Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans","volume":"13","author":"Zhao","year":"2025","journal-title":"PeerJ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"83446","DOI":"10.1109\/ACCESS.2023.3300771","article-title":"Intracranial hemorrhage classification from CT scan using deep learning and Bayesian optimization","volume":"11","author":"Arman","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, L., and Ji, X. 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