MATHEMATICAL MODELING FOR SECURING DIGITAL HEALTHCARE WITH CYBERSECURITY, LEGAL COMPLIANCE, AND TELEMEDICINE: AN OVERVIEW

Abstract

The rapid digitization of healthcare has introduced cybersecurity, legal, and telemedicine challenges, necessitating mathematical modeling for enhanced security, optimized telemedicine, and regulatory compliance. This study explores cryptographic algorithms, risk assessment models, and predictive analytics to mitigate cyber threats and protect patient data. Techniques such as stochastic modeling, game theory, and machine learning are examined for identifying vulnerabilities and improving system resilience, with case studies showcasing their effectiveness in cyberattack prevention, secure data transmission, and telemedicine optimization. Additionally, AI and blockchain are highlighted for their role in strengthening security and compliance. Despite these advancements, challenges like scalability, interoperability, and ethical concerns persist, emphasizing the need for interdisciplinary collaboration among healthcare professionals, cybersecurity experts, and policymakers. By integrating mathematical modeling with cybersecurity and legal frameworks, healthcare institutions can build secure, efficient, and regulation-compliant digital systems, reinforcing the essential role of mathematical frameworks in the future of digital healthcare security.