DIGITAL TWIN–BASED AI SIMULATION FOR PERSONALIZED DRUG SAFETY MONITORING IN TELEMEDICINE

The rapid growth of telemedicine has revolutionized healthcare delivery; however, real-time
drug safety monitoring in remote settings remains a major challenge. Adverse Drug
Reactions (ADRs), medication errors, and limited direct patient assessment can compromise
therapeutic outcomes. Digital Twin technology integrated with Artificial Intelligence (AI)
provides an innovative solution for personalized drug safety surveillance within telehealth
systems.
A Digital Twin represents a dynamic virtual model of a patient created using real-time
clinical data, laboratory values, physiological parameters, medication history, and genomic
information. Through machine learning and predictive analytics, the system can simulate
individual drug responses, predict potential adverse effects, optimize dosage regimens, and
detect drug–drug interactions before clinical manifestation. This predictive model shifts
pharmacovigilance from a reactive reporting approach to a proactive and preventive
framework.
In telemedicine, Digital Twin–based AI platforms can continuously monitor patient data,
generate automated safety alerts, and support clinical decision-making. This approach is
particularly beneficial for elderly patients, polypharmacy cases, and individuals with chronic
diseases requiring long-term therapy. Additionally, it enhances medication adherence,
reduces preventable hospitalizations due to ADRs, and supports regulatory compliance
through structured digital documentation.
Although challenges such as data privacy, interoperability, algorithm validation, and
infrastructure integration exist, Digital Twin–based AI simulation represents a transformative
advancement toward precision medicine and intelligent pharmacovigilance. This technology
has the potential to redefine personalized drug safety monitoring in modern telehealth
ecosystems.
Keywords: Digital Twin, Artificial Intelligence, Pharmacovigilance, Telemedicine,
Personalized Medicine