Enhancing Cyber Security In Healthcare -With The Help Of Machine Learning

As technology advances, the healthcare industry increasingly relies on electronic systems to store patient information. While this has made data access more accessible, it has also exposed healthcare organizations to cyber threats that can compromise sensitive information. In this forum, let’s delve into the use of machine learning to fortify cyber security in healthcare.

Machine learning is a form of artificial intelligence that enables systems to learn and improve from experience without explicit programming. This technology has been widely utilized in various healthcare sectors to pinpoint anomalies and patterns that could indicate potential security breaches.

Machine learning can enhance cyber security in healthcare through predictive modeling. By scrutinizing data patterns, algorithms can identify potential security threats and alert healthcare organizations to thwart them (Lu, Kaur, Thakur, & Arya, 2023).

Anomaly detection is another way machine learning can boost cyber security in healthcare. It involves analyzing data to identify patterns that deviate from the norm, which could indicate potential cyber threats. Machine learning algorithms can be trained to recognize these anomalies and prompt healthcare organizations to take action.

Furthermore, machine learning can also enhance the accuracy and efficiency of fraud detection. By analyzing data from multiple sources, algorithms can identify patterns that indicate fraudulent activity and alert healthcare organizations to take action (Najmusher, Pal, Mallick, Choudhury, & Munaveer, 2023).

However, while machine learning can potentially enhance cybersecurity in healthcare, it is not a silver bullet solution. Healthcare organizations must also implement other best practices, such as data encryption and multi-factor authentication, to ensure the security of patient information (Eid & Zaki, 2023).

What are your musings on using machine learning to enhance cyber security in healthcare? Does it have the potential to fortify the security of patient information? What other best practices should healthcare organizations implement to ensure the safety of patient data? Let’s open up this discussion.

References

Eid, M. M., & Zaki, R. M. (2023). Classification of Student Performance Based on Ensemble Optimized Using Dipper Throated Optimization. Journal of Artificial Intelligence and Metaheuristics, 2(1), 36-6.

Lu, J., Kaur, M., Thakur, H. K., & Arya, R. K. (2023). Computational Intelligence in Analytics and Information Systems: 2-volume set.

Najmusher, H., Pal, I., Mallick, A., Choudhury, A. R., & Munaveer, A. (2023). A Survey on Artificial Intelligence Image Recognition Method based on Convolutional Neural Network Algorithm. Research and Reviews: Advancement in Robotics, 6(1), 7-14.