AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability
Ravikumar Vallepu1
- Independent researcher, Greensboro, North Carolina, United States.
Section:Research Paper, Product Type: Journal Paper
Volume-13 ,
Issue-3 , Page no. 9-15, Mar-2025
CrossRef-DOI: https://doi.org/10.26438/ijcse/v13i3.915
Online published on Mar 31, 2025
Copyright © Ravikumar Vallepu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Citation
IEEE Style Citation: Ravikumar Vallepu, “AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability,” International Journal of Computer Sciences and Engineering, Vol.13, Issue.3, pp.9-15, 2025.
MLA Citation
MLA Style Citation: Ravikumar Vallepu "AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability." International Journal of Computer Sciences and Engineering 13.3 (2025): 9-15.
APA Citation
APA Style Citation: Ravikumar Vallepu, (2025). AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability. International Journal of Computer Sciences and Engineering, 13(3), 9-15.
BibTex Citation
BibTex Style Citation:
@article{Vallepu_2025,
author = {Ravikumar Vallepu},
title = {AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2025},
volume = {13},
Issue = {3},
month = {3},
year = {2025},
issn = {2347-2693},
pages = {9-15},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5777},
doi = {https://doi.org/10.26438/ijcse/v13i3.915}
publisher = {IJCSE, Indore, INDIA},
}
RIS Citation
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v13i3.915}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5777
TI - AI’s Transformative Role in Healthcare Data Management: Enhancing Governance, Security, and Interoperability
T2 - International Journal of Computer Sciences and Engineering
AU - Ravikumar Vallepu
PY - 2025
DA - 2025/03/31
PB - IJCSE, Indore, INDIA
SP - 9-15
IS - 3
VL - 13
SN - 2347-2693
ER -
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Abstract
Artificial intelligence is revolutionizing health data management. It strengthens governance, security, and interoperability. With the explosion of data in medical treatment, AI-driven solutions greatly facilitate data processing speed, reduce errors, and ensure compliance with standards. By automating quality control processes, AI is transforming data governance. Security tokens obstruct unwanted access to network assets (VPNs and anomaly detection systems are completed). They also enable dialogue between incompatible healthcare systems, allowing them to interact with each other even when one system cannot recognize the commands or parameters sent by another system to achieve communication within heterogeneous environments. Furthermore, through real-time clinical decision-making, AI addresses problems that may arise from integrating data from multiple sources or attempting to standardize everything in order to create better patient care outcomes. For all these reasons, the potential of AI to build a healthcare ecosystem that is resilient for the future and ready for tomorrow emerges clearly.
Key-Words / Index Term
Artificial Intelligence (AI), Healthcare, Data Management, Data Governance, Security and Privacy and Regulatory Compliance
References
[1] Topol E. J. High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine, Vol.25, Issue.1, pp.44–56, 2019. https://doi.org/10.1038/s41591-018-0300-7
[2] Picard R. W., Affective Computing, MIT Press, 1997.
[3] Rieke N., Hancox J., Li W., et al. The future of digital health with federated learning, npj Digital Medicine, Vol.3, No.119, 2020. https://doi.org/10.1038/s41746-020-00323-1
[4] Mandl K.D., Kohane I.S., Time for a patient-driven health information economy?, New England Journal of Medicine, Vol.374, pp.595–598, 2016. https://doi.org/10.1056/NEJMp1511931
[5] Wang F., Casalino L. P., Khullar D., Deep learning in medicine promise, progress, and challenges, Circulation: Cardiovascular Quality and Outcomes, Vol.11, Issue.10, 2018. https://doi.org/10.1161/CIRCOUTCOMES.118.004723
[6] Van der Schaar M., et al. How artificial intelligence is changing clinical development, The Lancet Digital Health, Vol.3, Issue.11, pp.e599–e610, 2021. https://doi.org/10.1016/S2589-7500(21)00170-3
[7] Wong T.Y., Bressler N. M., Artificial intelligence in ophthalmology: A review, Progress in Retinal and Eye Research, Vol.72, 2019, https://doi.org/10.1016/j.preteyeres.2019.04.003
[8] Koller D., Friedman N., Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
[9] Johan H., Federated Learning in Healthcare: Decentralized Intelligence for Data Privacy, In Proceedings of the 2022 IEEE International Conference on Healthcare Informatics, Singapore, pp.112–118, 2022.
[10] McCradden M. D., et al. Ethical concerns around use of AI in health care, Canadian Medical Association Journal (CMAJ), Vol.191, Issue.9, pp.E257–E258, 2019. https://doi.org/10.1503/cmaj.181947