Abstract :

The healthcare industry in the US is rapidly becoming digital, and as a result, there is a constant battle against cybersecurity risks utilizing sophisticated machine learning (ML) algorithms. The healthcare business is confronted with a formidable array of cybersecurity threats, as the sophistication of data breaches continues to rise and conventional security solutions fall short in safeguarding vital healthcare systems and sensitive patient information. Improving the overall resilience of a hospital digital infrastructure, ML driven solutions have recently arisen with substantial benefits for safeguarding vital healthcare systems, such as the ability to detect threats in real-time, identify anomalies, and do predictive analytics. To guarantee the dependability and ethical applications of ML technologies, it is necessary to address the concerns of data privacy, algorithm bias, and deployment complexity, notwithstanding these advantages. The study de