International Peer-Reviewed JournalOpen AccessISSN 2456-8880
irejournals@gmail.com+91-7433024337

Home / Current Issue / Paper 1706143

1706143 Vol 8 · Issue 2 Download Paper

Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients

Oladoyin Akinsuli

Subject area: Science,Engineering and Technology  ·  Area of research: AI in Medical Research

Abstract

Artificial intelligence (AI) is now rapidly impacting the medical industry to bring precise medicine from large data analysis for treatment. Over time, AI integration is better at defining the trajectory of treatment based on individual aspects of a patient, including genetic predispositions, habits, and illness history. Nevertheless, the new paradigm for applying AI generates security risks that should be resolved to protect patients and their information. This article presents the imperative terms for safeguarding AI systems in medicine, such as data security and the inviolability of algorithms' AI. Available patient data is very vulnerable to breaches, competitive incursions, as well as unfair suffering from algorithmic bias. To ensure that Personal Health Information is not misused especially where artificial intelligence is being adopted in the health care sector, AI models have to be protected against these threats. Furthermore, as discussed with the advancement of AI technologies in the field, it is necessary to work on safe setups that mean accurate data and confidentiality in patients' data simultaneously with emerging technologies that contribute to developing personalized medical systems. This is done through the science of layout of privacy regulations, ethical standards, and top-of-the-line security measures to prevent the rampant use of AI in healthcare organizations. Thus, security concerns that are tightly associated with the use of artificial intelligence can be centrally controlled, and healthcare by profiting from the opportunities that are presented by artificial intelligence can offer individual treatment and fine tune medical therapy for the patients to the extent that it would optimize the efficiency of medical research. Autonomous cars, smart homes, and embedded systems require security architecture to ensure that patient data is protected and that the data yielded by intelligent algorithms is trustworthy. Future possibilities, current regulatory barriers, and addressing such barriers using AI-secured technologies while improving patient-specific treatments are also examined in this article.

Keywords

Artificial intelligence, Personalized Medicine, Patient Data, Machine Learning, Drug Discovery

References

[1] Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... & Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint. https://arxiv.org/abs/1802.07228

[2] Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241-1250. https://doi.org/10.1016/j.drudis.2018.01.039

[3] Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2019). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241-1250. https://doi.org/10.1016/j.drudis.2018.01.039

[4] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/s41591-018-0316-z

[5] Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295-336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5

[6] Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial Intelligence in Healthcare (pp. 295-336). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00012-5

[7] Jha, S., Topol, E. J., & Desai, S. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 316(22), 2353-2354. https://doi.org/10.1001/jama.2016.17438

[8] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101

[9] Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., & Makowski, M. R. (2020). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 2(6), 305-311. https://doi.org/10.1038/s42256-020-0186-1

[10] Krittanawong, C., Johnson, K. W., Rosenson, R. S., Pinto, D. S., & Narula, J. (2019). Deep learning for cardiovascular medicine: A practical primer. European Heart Journal, 40(25), 2058-2073. https://doi.org/10.1093/eurheartj/ehz056

[11] Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., ... & Denniston, A. K. (2018). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271-e297. https://doi.org/10.1016/S2589-7500(19)30123-2

[12] Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181

[13] Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0301-6

[14] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259

[15] Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 1-7. https://doi.org/10.1038/s41746-020-00323-1

[16] Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 1-7. https://doi.org/10.1038/s41746-020-00323-1

[17] Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

[18] Ziller, A., Passerat-Palmbach, J., Ryffel, T., Trask, A., & Makowski, M. R. (2021). Privacy-preserving machine learning for medical imaging. Nature Machine Intelligence, 3(6), 474-484. https://doi.org/10.1038/s42256-021-00316-4

[19] Dave, N. Banerjee and C. Patel, "CARE: Lightweight attack resilient secure boot architecture with onboard recovery for RISC-V based SOC", Proc. 22nd Int. Symp. Quality Electron. Design (ISQED), pp. 516-521, Apr. 2021.

[20] Dave, N. Banerjee and C. Patel, "SRACARE: Secure Remote Attestation with Code Authentication and Resilience

[21] Engine," 2020 IEEE International Conference on Embedded Software and Systems (ICESS), Shanghai, China,

[22] 2020, pp. 1-8, doi: 10.1109/ICESS49830.2020.9301516.

[23] Dave, A., Wiseman, M., & Safford, D. (2021, January 16). SEDAT:Security Enhanced Device Attestation with TPM2.0. arXiv.org. https://arxiv.org/abs/2101.06362

[24] Dave, M. Wiseman and D. Safford, "SEDAT: Security enhanced device attestation with TPM2.0", arXiv:2101.06362, 2021.

[25] Avani Dave. (2021). Trusted Building Blocks for Resilient Embedded Systems Design. University of Maryland.

[26] Dave, N. Banerjee and C. Patel, "CARE: Lightweight attack resilient secure boot architecturewith onboard recovery for RISC-V based SOC", arXiv:2101.06300, 2021.

[27] Avani Dave Nilanjan Banerjee Chintan Patel. Rares: Runtime attackresilient embedded system design using verified proof-of-execution.arXiv preprint arXiv:2305.03266, 2023.

[28] Elemam, S. M., & Saide, A. (2023). A Critical Perspective on Education Across Cultural Differences. Research in Education and Rehabilitation, 6(2), 166-174.

[29] Rahman, M.A., Butcher, C. & Chen, Z. Void evolution and coalescence in porous ductile materials in simple shear. Int J Fracture, 177, 129–139 (2012). https://doi.org/10.1007/s10704-012-9759-2

[30] Rahman, M. A. (2012). Influence of simple shear and void clustering on void coalescence. University of New Brunswick, NB, Canada. https://unbscholar.lib.unb.ca/items/659cc6b8-bee6-4c20-a801-1d854e67ec48

How to cite this paper

Oladoyin Akinsuli "Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients" Iconic Research And Engineering Journals Volume 8 Issue 2 2024 Page 925-941
Oladoyin Akinsuli "Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients" Iconic Research And Engineering Journals, vol. 8, no. 2, Aug. 2024
Oladoyin Akinsuli (2024). Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients. Iconic Research And Engineering Journals, 8(2).
Oladoyin Akinsuli "Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients" Iconic Research And Engineering Journals, vol. 8, no. 2, Aug. 2024.
@article{1706143,
      author = {Oladoyin Akinsuli},
      title = {Securing AI in Medical Research: Revolutionizing Personalized and Customized Treatment for Patients},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {925-941},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706143.pdf},
      abstract = {Artificial intelligence (AI) is now rapidly impacting the medical industry to bring precise medicine from large data analysis for treatment. Over time, AI integration is better at defining the trajectory of treatment based on individual aspects of a patient, including genetic predispositions, habits, and illness history. Nevertheless, the new paradigm for applying AI generates security risks that should be resolved to protect patients and their information.
This article presents the imperative terms for safeguarding AI systems in medicine, such as data security and the inviolability of algorithms' AI. Available patient data is very vulnerable to breaches, competitive incursions, as well as unfair suffering from algorithmic bias. To ensure that Personal Health Information is not misused especially where artificial intelligence is being adopted in the health care sector, AI models have to be protected against these threats.
Furthermore, as discussed with the advancement of AI technologies in the field, it is necessary to work on safe setups that mean accurate data and confidentiality in patients' data simultaneously with emerging technologies that contribute to developing personalized medical systems. This is done through the science of layout of privacy regulations, ethical standards, and top-of-the-line security measures to prevent the rampant use of AI in healthcare organizations.
Thus, security concerns that are tightly associated with the use of artificial intelligence can be centrally controlled, and healthcare by profiting from the opportunities that are presented by artificial intelligence can offer individual treatment and fine tune medical therapy for the patients to the extent that it would optimize the efficiency of medical research. Autonomous cars, smart homes, and embedded systems require security architecture to ensure that patient data is protected and that the data yielded by intelligent algorithms is trustworthy. Future possibilities, current regulatory barriers, and addressing such barriers using AI-secured technologies while improving patient-specific treatments are also examined in this article.},
      keywords = {Artificial intelligence, Personalized Medicine, Patient Data, Machine Learning, Drug Discovery},
      month = {August},
  }