Imagine if a surgeon told you before your surgery, “I am performing the same procedure on your replica,” so we can identify how your body reacts. You might not believe doctors because it seems impossible that a replica could undergo the same surgery and react the same way as your real body. Welcome to the new era called digital twin technology.
A digital twin is a replica of a physical object, system, or virtual human model that uses real-time data to accurately reflect its real-world counterpart’s behaviour, performance, and condition. It is an innovative technology that has a significant impact on the healthcare industry by predicting physiological and behavioral responses of the human body, as well as targeted treatments and interventions to improve health outcomes.
Let’s dive deeper into the world of digital twins, how they work, their benefits, challenges, and limitations. How’s India’s growing digital twin research ecosystem?
How Digital Twin Works and Its Benefits
Digital twin technology works by collecting real-time health data from patients through multiple sources. Later, with the help of artificial intelligence and machine learning, patients’ data is combined to build a multidimensional virtual model. Genomic and lifestyle data are the most important part of this technology. The virtual model is continuously updated with the real-world data streams. Doctors and researchers can monitor the virtual model to understand how the organ responds to new drugs, treatments, or surgical interventions. Run different scenarios with different conditions. Based on the results from the simulation, doctors improve the treatment plan and system. Later, when the real patient is treated, the real-world outcomes further refine the digital twin over time.
Benefits:
- Personalized and precise treatment planning
- Improved diagnosis accuracy
- Real-time monitoring & early warning
- Predictive simulation & disease modeling
- Enhanced surgical planning
- Optimized chronic disease management
- Improved clinical decision support
- Accelerated drug and device development
- Hospital operations optimization
- Population health management
Data Sources for Health Digital Twins
- Wearable devices and biosensors: Patient data such as heart rate, sleep duration, steps, and activity levels taken from the smart watches and fitness trackers. ECG, blood glucose, oxygen saturation, temperature, and other biometric signals; these patients’ data can be sourced from medical-grade wearables. Long-term monitoring of patients can be possible with the help of smart patches that track hydration level, muscle activity, posture, and body temperature.
- Electronic Health Records (EHRs): EHRs contain patients’ data, clinical information, medical history, radiology reports, and recommended medications. Genomic data is also obtained from a specific patient, leading to improved and faster outcomes with the application of precision medicine.
- Lifestyle and behavioural data: Patients’ food intake will be tracked, obtained from nutrition apps, and lifestyle, exercise routine, mental health, and mood will be monitored and used in digital twin technology.
- Real-time sensor data: Data should be obtained from smartphones, like app usage, gyroscope, accelerometer, voice analysis, tone rhythms, and emotional cues used in mental health monitoring.
- Environmental and contextual data: Location data such as temperature, pollution readings, altitude, air quality, travel patterns, daily routines, etc.
- Social and psychological data: Data such as behaviour insights in social gatherings, stress levels, memory tests, reaction times, attention span metrics, etc.
Digital Twin Healthcare Research and Ecosystem Programs in India
India’s digital twin healthcare ecosystem is supported by national research missions, innovation hubs, and academic collaborations focused on precision medicine, AI-driven healthcare modeling, and predictive health systems:
| Program / Initiative | Organization | Program Type | Digital Twin Role |
| Charak DT Human Digital Twin Platform | IIT Indore (DRISHTI CPS Foundation) | Human Digital Twin R&D Platform | Develops AI-powered virtual human models for disease prediction and precision medicine |
| National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS) | Department of Science and Technology (Government of India) | National Research Mission | Funds digital twin, AI, and cyber-physical healthcare research programs |
| DRISHTI CPS Technology Innovation Hub | IIT Indore | Digital Twin Research Hub | Supports the development of healthcare digital twins, simulation platforms, and predictive models |
| Centre of Excellence in Precision and Personalised Healthcare | IIT Delhi | Precision Medicine Research Center | Enables AI-driven personalized healthcare and patient-specific modeling |
| AIIMS–IIT Indore Healthcare Technology Collaboration | AIIMS + IIT Indore | Clinical Research and Validation Collaboration | Supports real-world clinical validation of digital twin and AI healthcare systems |
| Smart Healthcare Technology Research Collaboration | IIT Roorkee + Patanjali University | Academic Research Collaboration | Advances in AI-driven healthcare modeling and smart healthcare technologies |
| Ayushman Bharat Digital Mission (ABDM) | Government of India | National Digital Health Infrastructure | Provides an integrated national health data infrastructure enabling digital twin systems |
| Digital Twin Applications in Drug Development | Pharma companies and research institutions | Clinical Research and Simulation Programs | Uses virtual patient models to simulate drug response and accelerate clinical trials |
Digital Twin Healthcare Platforms and Solution Providers in India
| Organization / Platform | Digital Twin Focus Area | Key Use Case | Ecosystem Role |
| Charak DT Platform (DRISHTI CPS Foundation, IIT Indore) | Human Digital Twin Platform | Disease prediction, preventive diagnosis, precision medicine | Core digital twin platform developer |
| Faststream Technologies Digital Twin Platform | Patient Digital Twin Platform | Virtual patient modeling and clinical decision support | Digital twin solution provider |
| Tata Consultancy Services (TCS) Digital Twin Solutions | Healthcare Digital Twin Infrastructure | Hospital simulation and healthcare operations optimization | Enterprise digital twin provider |
| Infosys Digital Twin Framework | Healthcare Simulation Infrastructure | Precision medicine simulation and healthcare system modeling | Digital twin infrastructure provider |
| Siemens Healthineers Digital Twin Technology | Medical Device Digital Twins | Imaging system simulation and diagnostic optimization | Medical digital twin provider |
| Ayushman Bharat Digital Mission (ABDM) | Digital Health Data Infrastructure | Provides standardized health data for digital twin modeling | Foundational infrastructure provider |
| Pharmaceutical Digital Twin Simulation Programs | Drug Development Digital Twins | Virtual clinical trials and drug response simulation | Clinical digital twin application provider |
Challenges & Limitations
Despite strong potential, digital twins in healthcare face significant technical, clinical, ethical, and operational limitations that currently restrict large-scale adoption.
Data quality and interoperability: Data quality and interoperability are major concerns because digital models heavily rely on high-quality, longitudinal, and multimodal patient data.
High cost: Implementing this technology requires advanced computing infrastructure, cloud & edge processing, and a specialized AI expert, which increases financial load on healthcare organizations.
Privacy and ethical concerns: Digital twin models are virtual replicas of a human body that contain a patient’s information, which leads to data privacy concerns and cybersecurity risks.
Regulatory & legal uncertainty: The lack of strict regulations and clarity in legal risk for hospitals and developers leads to slow commercialization.
Future potential
Unlike other innovations, the digital twin is still an emerging technology. Many research studies are underway on digital twins. We can witness many achievements in this field in the coming years. With the support of the Indian government and companies, we can see more real-world applications of digital twins that will enhance patient care.