Methods and Algorithms of Ensuring Data Privacy in AI-Based Healthcare Systems and Technologies

Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

Abstract:

Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, these projects propose AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project present the best-in-school techniques used to preserve data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptography techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures, and identifies potential correction/mitigation measures.

Keywords: Data privacy, artificial intelligence, healthcare AI, data sharing, healthcare organizations.

References:

[1] Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Highly accurate protein structure prediction with AlphaFold Nature, 2021 596 (7873) pp. 583-589
[2] Milana C., Ashta A. Artificial intelligence techniques in finance and financial markets: A survey of the literature. 2021. 30 (3) pp. 189-209
[3] Statista. Artificial intelligence (AI) in healthcare market size worldwide from 2021 to 2030. 2024. Available at: https://www.statista.com/statistics/1334826/ai-in-healthcare-market-size-worldwide/#:~:text=It%20was%20forecast%20that%20the
[4] Vemuri, Naveen. AI-Optimized DevOps for Streamlined Cloud CI/CD. International Journal of Innovative Science and Research Technology: 2024. 9.7.10.5281/zenodo.10673085.
[5] National Intelligence Council. Global Trends 2040: A More Contested World, 2021. COSIMO REPORTS. 9781646794973, 1646794974
[6] Hutan Ashrafian, Niklas Lidströmer. Artificial Intelligence in Medicine. 2022. Springer International Publishing.
[7] Halling-Brown M. D., Warren L. M., Ward D., Lewis E., Mackenzie A., Wallis M. G., et al. OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data. Radiol Artif Intell. 2021; 3:e200103.
[8] Murdoch B. Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Med Ethics. 2021; 22:122.
[9] Moshawrab M., Adda M., Bouzouane A., Ibrahim H., Raad A. Reviewing federated machine learning and its use in diseases prediction. Sensors (Basel) 2023; 23:2112.
[10] Sailakshmi V. Analysis of Cloud Security Controls in AWS, Azure, and Google Cloud; 2021.
[11] Kayoe, S., & Godwin, O. Examining the effects of worldwide developments, such as the emergence of online learning and the growing emphasis on global cooperation: 2023.
[12] Nyathani, Ramesh. Integration of Industry 4.0 and Human Resources: Evolving Human Capital Management and Employee Experience through Digital Innovations: 2022.
[13] Kayoe, S., & Godwin, O. Examining the effects of worldwide developments, such as the emergence of online learning and the growing emphasis on global cooperation: 2023.
[14] Bernd Carsten Stahl. Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies. 2021. Springer International Publishing
[15] Kalla, Dinesh & Samaah, Fnu & Kuraku, Sivaraju & Smith, Nathan. Phishing Detection Implementation Using Databricks and Artificial Intelligence. SSRN Electronic Journal. 2023. 185. 10.2139/ssrn.4452780.
[16] Chowdhury D., Dey A., Garai R., Adhikary S., Dwivedi A.D., Ghosh U., Alnumay W.S. Decrypt: A 3DES inspired optimised cryptographic algorithm J. Ambient Intell. Humaniz. Comput. (2022), pp. 1-11
[17] Paul J., Annamalai M. S. M. S., Ming W., Al Badawi A., Veeravalli B., Aung K.M.M. Privacy-preserving collective learning with homomorphic encryption IEEE Access, 9 (2021), pp. 132084-132096
[18] Rasheed K., Qayyum A., Ghaly M., Al-Fuqaha A., Razi A., Qadir J. Explainable, trustworthy, and ethical machine learning for healthcare: A survey Comput. Biol. Med. (2022), Article 106043
[19] World Health Organization Ethics and Governance of Artificial Intelligence for Health. (Accessed on 24 February 2024). Available online: https://www.who.int/publications/i/item/9789240029200
[20] Zeng D., Cao Z., Neill D. B. (2021). Artificial Intelligence in Medicine: Artificial intelligence–enabled public health surveillance, From local detection to global epidemic monitoring and control; Academic Press; Cambridge, MA, USA: pp. 437–453.
[21] Akgün M., Pfeifer N., Kohlbacher O. Efficient privacy-preserving whole genome variant queries Bioinformatics (2022)
[22] Caroline Brogan. New AI technology protects privacy in healthcare settings. (2021). Available at: https://www.imperial.ac.uk/news/222093/new-ai-technology-protects-privacy-healthcare/
[23] IBM. IMB Watson Health. (Accessed on 27 February 2024). Available online: https://www.ibm.com/watson-health.
[24] Bass D. Microsoft Develops AI to Help Cancer Doctors Find the Right Treatments. (Accessed on 27 February 2024). Available online: https://www.bloomberg.com/news/articles/2016-09-20/microsoft-develops-ai-to-help-cancer-doctors-find-the-right-treatments
[25] Work Report of the Supreme People’s Court. At the Fifth Session of the Thirteenth National People’s Congress on 8 March 2022. (Accessed on 28 February 2024); Available online: https://www.court.gov.cn/zixun-xiangqing-349601.html
[26] Ali A., Ilahi I., Qayyum A., Mohammed I., Al-Fuqaha A., Qadir J. Incentive-driven federated learning and associated security challenges: A systematic review (2021)
[27] Torkzadehmahani R., Nasirigerdeh R., Blumenthal D. B., Kacprowski T., List M., Matschinske J., Privacy-Preserving artificial intelligence techniques in Biomedicine. Methods Inf Med. (2022). 61:e12–27.