In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.

• Chapter 1: Introduction: digital twin technology in healthcare
• Chapter 2: Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics
• Chapter 3: Application of digital twin technology in model-based systems engineering
• Chapter 4: Digital twins in e-health: adoption of technology and challenges in the management of clinical systems
• Chapter 5: Digital twin and big data in healthcare systems
• Chapter 6: Digital twin data visualization techniques
• Chapter 7: Healthcare cyberspace: medical cyber physical system in digital twin
• Chapter 8: Cloud security-enabled digital twin in e-healthcare
• Chapter 9: Digital twin in prognostics and health management system
• Chapter 10: Deep learning in Covid-19 detection and diagnosis using CXR images: challenges and perspectives
• Chapter 11: Case study: digital twin in cardiology