Digital Twins in Healthcare: Necessities, Pros, Cons, and Future Directions

Introduction: Digital Twin (DT) refers to a digital model of a physical product, system, or process used for simulation, monitoring, and optimization. In healthcare, this technology has transformed patient care by generating virtual models of patients, enabling the prediction and optimization of health outcomes. This study examines the benefits, challenges, implementation requirements, and future directions of this technology in healthcare, aiming to review and summarize prior studies while avoiding duplication of research. Method: This study employs a narrative approach in the context of digital twins in healthcare. It does not follow a systematic methodology but rather relies on existing models, hypotheses, and personal expertise to derive general conclusions. Results: This study describes how DT has facilitated personalized medicine, improved surgical outcomes, managed chronic diseases, streamlined clinical trials, and optimized hospital operations. Additionally, it highlights challenges associated with DT in healthcare, such as ethical implications, privacy concerns, and regulatory issues. It emphasizes the need for strong data governance and interdisciplinary collaboration to fully leverage the potential of DT. Finally, this research explores key areas for the future development of this technology. Conclusion: The requirements, challenges, benefits, and future directions presented in this study can guide the design and implementation of accurate and intelligent DT in the healthcare domain