Enabling Personalized Care through Digital Twin Technology

This AI-powered diagnostic software leverages the digital twin technology in the metaverse to hyperpersonalize diagnoses and treatments.


Diagnosis & Treatment

Personalized Medicine


There’s nothing universal about the medication we consume

Multiple studies have established how standardized and approved medications have been ineffective on patients globally. The percentage of patients for whom medications are ineffective ranges from 38-75% for varying conditions from depression to osteoporosis.

The main cause behind ineffective drugs is the very specific genetic makeup of every individual. The latter is so different and their interaction so unique that therapies for an average patient may not be well-suited to the actual patient – not to mention how drug testing is not representative.Overall percentage of patients on whom medication is ineffective for long-term diseases

Global researchers and healthcare practitioners have realized the power of digital twins to boost precision medicine – an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person to maximize efficacy and efficiency.

A digital twin is a virtual model or simulation of any object, process, or system that is created using real-world data to learn more about its real-world counterpart. In the healthcare metaverse, the patient’s digital twin is the patient themself.


Time to ditch the cookie-cutter method of formulating medications

The ‘one-size-fits-all’ approach to disease treatment fails to consider differences between individual treatments, reducing chances of survival and increasing patients’ exposure to adverse effects.


Custom medication that’s formulated for the individual

Designing the UX of diagnostic software using digital twin technology to customize diagnosis and treatment for patients by integrating patient-centric data analysis into clinical decision-making.

Creating a digital twin involves the collection and synthesis of data from various sources


Leveraging digital twin technology to deliver personalized medication

The healthcare industry is constantly striving to enhance patient outcomes, reduce operating costs, and address unforeseen medical crises effectively. We wanted to create a conceptual diagnostic tool using quick, iterative testing to collect real-time data through sensors and reflect them into digital devices – a novel approach for healthcare diagnostics.


MVP: Creating a viable solution in a short span

Koru’s MVP design process is iterative, using feedback loops to address user needs.

The Minimum Viable Product (MVP) design approach is an iterative process based on constant user feedback. It aims to tackle problems and address needs in unique ways. It is an opportunity to set a benchmark in the industry.

We followed the Minimum Viable Product (MVP) design approach to create a viable, solution in a short span. It is an iterative process based on constant user feedback and remains user-focused throughout. It aims to tackle problems or address needs in unique ways. It is an opportunity to set a benchmark in the industry.

This process workflow depicts the steps from login to running the drug simulation

Creating a digital twin of any physical asset involves the collection and synthesis of data from various sources including physical data, manufacturing data, operational data, and insights from analytics software. The consistent flow of data is key to acquiring the best possible analysis and insights regarding the asset which helps in optimizing the outcome, be it improving clinical decision-making ability, customizing treatments and drug administration, disease modeling, planning surgical procedures, or testing new medical devices and drugs.

Research Methods Used

  • Process mapping
  • Case studies and report referrals

Designing a digital twin in the metaverse
The module is designed to capture continuous data from the individual about various vitals, medical conditions, and responses to the drug, therapy, and surrounding ecosystem. Historic and real-time data of each patient helps the ML algorithm predict future health conditions and analysis of drug trials. This module leverages a large amount of rich data from various IoT devices and uses AI-powered models to develop more personalized and improved solutions.

Concept Design

Key Highlights

  • The module captures continuous data from an individual patient about various vitals, medical conditions, and healthcare history to create a digital twin.
  • It runs an ML algorithm that suggests the best treatment options for trial on the digital twin.
  • The system leverages the gathered data and runs trials on the digital twin to determine the right therapy and predict the outcome of a specific procedure.
  • It comes up with a comparative analysis of the best possible treatment and suggests an optimally effective solution based on precision medicine.

Winner of the iF Design Award 2023

User Experience category

The digital twin module is designed to enhance treatment options and accelerate trial timelines


  • With clinical evidence and real-world data, this digital twin module is designed to create simulations of new treatments at a 24% faster rate.

  • It could be used to analyze the progression of neurogenerative ailments such as Alzheimer’s and Parkinson’s and accelerate treatment timelines.

1500+ healthcare UX projects completed for startups to industry leaders

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