Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential
to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive
review explores the current landscape of IPS, encompassing various technological approaches, applications,
benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques,
and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers,
and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision
support systems, and real-time patient data, IPS generates personalized treatment recommendations that
enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS
include predictive modeling, Drug-Drug Interaction detection, adverse event prediction, dose optimization,
and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate
the complexities of medication management, particularly in the context of polypharmacy and chronic disease
management. While IPS holds immense promise for improving patient care and reducing healthcare
costs, several challenges must be addressed. These include data privacy and security concerns, interoperability
issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally,
the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare
regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital
health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains
momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading
to more effective, personalized, and patient-centric healthcare delivery.
