Academic Journal of Business & Management, 2026, 8(4); doi: 10.25236/AJBM.2026.080414.
Fugen Zhou1, Shangying Zhou2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
2School of Business, University of Shanghai for Science and Technology, Shanghai, China
With the rapid development of artificial intelligence (AI) technologies in the healthcare sector, medical imaging data have been increasingly applied in disease diagnosis, clinical decision-making, and medical research, becoming a critical foundational resource for the advancement of smart healthcare. However, due to their high sensitivity and identifiability, the large-scale circulation of medical imaging data across processes such as collection, storage, processing, and sharing has significantly intensified privacy security risks. From the perspective of the data lifecycle, this study systematically analyzes the privacy risks associated with medical imaging data at different stages under the AI context and explores their underlying causes from technological, institutional, and behavioral dimensions. On this basis, a comprehensive governance framework is constructed, encompassing technological governance, institutional governance, organizational governance, and multi-stakeholder collaborative governance. The findings indicate that privacy risks of medical imaging data exhibit full lifecycle penetration and multi-factor coupling characteristics. Therefore, achieving a balance between data utilization and privacy protection requires a multi-level coordinated governance approach. This study provides theoretical insights for improving healthcare data governance systems and promoting the sustainable development of intelligent healthcare.
Artificial Intelligence, Medical Imaging Data, Privacy Security, Data Governance
Fugen Zhou, Shangying Zhou. Privacy Security Risks and Governance Mechanisms of Medical Imaging Data in the Context of Artificial Intelligence. Academic Journal of Business & Management (2026), Vol. 8, Issue 4: 105-110. https://doi.org/10.25236/AJBM.2026.080414.
[1] Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22.
[2] Rempe, M., Heine, L., Seibold, C., Hörst, F., & Kleesiek, J. (2024). De-identification of medical imaging data: a comprehensive tool for ensuring patient privacy. European Radiology, 35, 7809 - 7818.
[3] Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M., Saleh, A., Makowski, M., Rueckert, D., & Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3, 473 - 484.
[4] Khalifa, M., & Albadawy, M. (2024). AI in Diagnostic Imaging: Revolutionising Accuracy and Efficiency. Computer Methods and Programs in Biomedicine Update.
[5] Chen, Z., Lou, Y., Wang, B., Lei, H., & Yang, P. (2024). Application of Cloud-Driven Intelligent Medical Imaging Analysis in Disease Detection. Journal of Theory and Practice of Engineering Science.
[6] Holub, P., Müller, H., Bíl, T., Pireddu, L., Plass, M., Prasser, F., Schlünder, I., Zatloukal, K., Nenutil, R., & Brázdil, T. (2022). Privacy risks of whole-slide image sharing in digital pathology. Nature Communications, 14.
[7] S, G., & S, G. (2023). Securing medical image privacy in cloud using deep learning network. Journal of Cloud Computing, 12, 1-15.
[8] Kaissis, G., Makowski, M., Rückert, D., & Braren, R. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2, 305 - 311.
[9] Chen, Y., & Esmaeilzadeh, P. (2024). Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges. Journal of Medical Internet Research, 26.