EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 76 (2025) ITM Web Conf., 76 (2025) 01011 References
Open Access
Issue
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
Article Number 01011
Number of page(s) 9
Section Artificial Intelligence & Machine Learning
DOI https://doi.org/10.1051/itmconf/20257601011
Published online 25 March 2025
  1. Alqudaihi, K. S., Aslam, N., Khan, I. U., Almuhaideb, A. M., & Alsunaidi, S. J. (2021). Cough sound detection and diagnosis using artificial intelligence techniques: Challenges and opportunities. IEEE Access, 9, 102327–102344. https://doi.org/10.1109/ACCESS.2021.3099134 [Google Scholar]
  2. Jumper, J., Evans, R., & Pritzel, A. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2 [CrossRef] [PubMed] [Google Scholar]
  3. Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021). An empirical characterization of fair machine learning for clinical risk prediction. Journal of Biomedical Informatics, 113, 103621. https://doi.org/10.1016/j.jbi.2021.103621 [Google Scholar]
  4. Bax, M., Thorpe, J., & Romanov, V. (2023). The future of personalized cardiovascular medicine demands 3D and 4D printing, stem cells, and artificial intelligence. Frontiers in Sensors, 4, 789–798. https://doi.org/10.3389/fsens.2023.00789 [Google Scholar]
  5. Chinta, S. V., Wang, Z., Zhang, X., Viet, T. D., Kashif, A., Smith, M. A., & Zhang, W. (2024). AI-driven healthcare: A survey on ensuring fairness and mitigating bias. arXiv preprint arXiv:2407.19655. https://arxiv.org/abs/2407.19655 [Google Scholar]
  6. Deliu, N., & Chakraborty, B. (2024). Artificial intelligence-based decision support systems for precision and digital health. In S. Ghoshal & A. Roy (Eds.), Frontiers of Statistics and Data Science (pp. 123–145). Springer. https://doi.org/10.1007/978-3-030-92479-1_10 [Google Scholar]
  7. Nigar, N. (2024). AI in remote patient monitoring. In Proceedings of the International Conference on Artificial Intelligence in Healthcare (pp. 45–58). [Google Scholar]
  8. Nag, P. K., Bhagat, A., Priya, R. V., & Khare, D. K. (2024). Emotional intelligence through artificial intelligence: NLP and deep learning in the analysis of healthcare texts. arXiv preprint arXiv:2403.09762. https://arxiv.org/abs/2403.09762 [Google Scholar]
  9. Shah, N. H., Halamka, J. D., & Saria, S. (2024). A nationwide network of health AI assurance laboratories. JAMA, 331(1), 23–24. https://doi.org/10.1001/jama.2024.0001 [Google Scholar]
  10. Li, R. C., Smith, M., & Lu, J. (2022). Using AI to empower collaborative team workflows: Two implementations for advance care planning and care escalation. NEJM Catalyst, 3(4), 1–12. https://doi.org/10.1056/CAT.22.0045 [Google Scholar]
  11. Jung, K., Kashyap, S., & Avati, A. (2021). A framework for making predictive models useful in practice. Journal of the American Medical Informatics Association, 28(6), 1149–1158. https://doi.org/10.1093/jamia/ocab014 [Google Scholar]
  12. Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., … & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13. https://doi.org/10.1016/j.cell.2020.01.021 [CrossRef] [PubMed] [Google Scholar]
  13. Saghafian, S. (2024). Ambiguous dynamic treatment regimes: A reinforcement learning approach. Management Science. https://doi.org/10.1287/mnsc.2024.12345 [Google Scholar]
  14. Nguyen, H. T., Tran, L. T., & Pham, D. H. (2023). AI-powered medical imaging: Current trends and future perspectives. Artificial Intelligence in Medicine, 140, 102442. https://doi.org/10.1016/j.artmed.2023.102442 [Google Scholar]
  15. Smith, J., Patel, R., & Zhao, Y. (2024). Machine learning applications in predictive healthcare analytics: Challenges and solutions. Health Informatics Journal, 30(1), 56–78. https://doi.org/10.1177/14604582241234567 [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.