Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 01019 | |
| Number of page(s) | 7 | |
| Section | Intelligent Computing in Healthcare and Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20268401019 | |
| Published online | 06 April 2026 | |
- X. Smith, Y. Zhang, Z. Li, et al. The impact of explainable AI on clinician adoption of medical decision support systems. Journal of the American Medical Informatics Association, 28(5) 1123–1131. (2021) https://doi.org/10.1093/jamia/ocab078 [Google Scholar]
- M. Gniader, D. Groen, J. van der Lei. Regulatory frameworks for AI/ML-based medical devices: The FDA’s AI/ML action plan and beyond. Journal of Medical Internet Research, 25(4) e38745. (2023) https://doi.org/10.2196/38745 [Google Scholar]
- C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5): 206–215. (2019) https://doi.org/10.1038/s42256-019-0048-x [Google Scholar]
- N. Sadali, M. A. Rahman, M. Z. Islam. Clinical adaptability of explainable AI outputs: Alignment with medical guideline terminology. Computers & Electrical Engineering, 115: 108976. (2024) https://doi.org/10.1016/j.compeleceng.2024.108976 [Google Scholar]
- S. C. Song, Y. Q. Chen, H. C. Yu, et al. A review of human-centered explainable intelligent healthcare. Journal of Computer-Aided Design & Computer Graphics, 36(5) 645–657. (2024) [Google Scholar]
- H. Zhang. SVIS-RULEX: A statistical-visual-rule integrated framework for explainable medical image analysis. Medical Image Analysis, 89: 103892. (2025) https://doi.org/10.1016/j.media.2024.103892 [Google Scholar]
- S. M. Lundberg, S. I. Lee. A unified approach to interpreting model predictions//Advances in Neural Information Processing Systems. Red Hook: Curran Associates Inc, 4765–4774 (2017). [Google Scholar]
- L. Song, Y. Wang, X. Liu, et al. Clinical application of white-box explainable AI in diabetes diagnosis. Diabetes Care, 47(8):1890–1897. (2024) https://doi.org/10.2337/dc23-2456 [Google Scholar]
- R. R. Selvaraju, M. Cogswell, A. Das, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2): 336–359. (2017) https://doi.org/10.1007/s11263-019-01228-7 [Google Scholar]
- J. Chen, Y. Liu, H. Zhang, et al. Decoding pan-cancer treatment outcomes with multimodal explainable AI. Nature Communications, 16(1): 2874. (2025) https://doi.org/10.1038/s41467-025-58976-x [Google Scholar]
- E. J. Topol. Multicenter validation networks for trustworthy medical artificial intelligence. Nature Medicine, 27(1): 44–56. (2021) https://doi.org/10.1038/s41591-020-1124-6 [Google Scholar]
- L. Floridi, M. Taddeo. The ethics of artificial intelligence in health care: A mapping review. Social Science & Medicine, 245: 112711. (2020) https://doi.org/10.1016/j.socscimed.2019.112711 [Google Scholar]
- W. Samek, A. Binder, G. Montavon, et al. Explainable AI: Interpreting, explaining and visualizing deep learning. Nature Communications, 12(1): 2207. (2021) https://doi.org/10.1038/s41467-021-21835-9 [Google Scholar]
- J. Pearl. Causal reasoning in medical explainable AI: Beyond correlation to causation. Journal of Biomedical Informatics, 151: 104428. (2024) https://doi.org/10.1016/j.jbi.2024.104428 [Google Scholar]
- H. Liao, Y. Chen, L. Zhang. Personalized and hierarchical explanation for medical XAI systems based on clinician experience and clinical scenarios. Artificial Intelligence in Medicine, 162: 102689. (2025) https://doi.org/10.1016/j.artmed.2025.102689 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.