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Your body parts will now have their AI-powered digital twins
India's largest IT services provider Tata Consultancy Services (TCS) is building a digital twin of the heart of US marathon runner Des Linden, who ran at the TCS-sponsored New York City Marathon on 5 November. The so-called digital bio-twin will provide Linden with real-time data on how her heart functions and responds to different conditions while training for, and running, marathons.
In an interview, Frank Diana, principal futurist at TCS, discussed the significance of digital twins across sectors, and shared his views on generative artificial intelligence (AI), artificial general intelligence (AGI), quantum computing, and synthetic biology.
A digital twin heart is a real-time, virtual replica of a person’s heart, offering precise data on its function, efficiency and response to varying conditions. Linden will have access to a tool that offers unprecedented insights into her cardiovascular health throughout her training. TCS is partnering with Dassault Systèmes to join the Living Heart Project, which unites cardiologists, researchers, educators, regulators and other experts to develop and validate realistic digital simulations of the human heart.
Source: NYT print ad of TCS Marathon; Picture courtesy of: TCS
According to Diana, if we can digitally mimic organs such as the heart, colon, skin and nose, and allow for simulation and some level of predictability, we will be able to solve big challenges in the Health and Wellness sector. Digital bio-twins can complement wearables, and the data that flows through them can help us better understand how the body reacts and responds to activities like running as in Linden’s case.
A digital twin can also help us become better prepared for disasters, whether it's a pandemic or hurricane or any extreme event. There are people even working on a digital twin of the Earth (to simulate, among other things, the atmosphere, ocean, ice and land, and provide forecasts of floods, droughts and fires). A digital twin in the education sector could help doctors train more efficiently for surgeries without the risks associated with surgeries. Children can be simulated through a digital twin from early childhood to the point where that digital twin knows a lot about that individual and helps nurture them through their formative years. To become more energy efficient, we can simulate buildings, cars etc.
We’re also seeing the convergence of digital twins with AI, and even the Metaverse. These building blocks, as Diana likes to call them, are accelerating the pace of innovation because they can converge in many ways. Diana cites an example. "I showed a video of a mother being reacquainted with her dead 8-year-old daughter virtually. The simulation is very real and emotional. Virtual reality combined with AI was leveraged to simulate her daughter, and haptic gloves allowed her (the mother) to touch her daughter in some sense. I asked the audience: If you could reconnect with a dead relative or friend, would you? Many people will say ‘No’ to that question, but it's this convergence of these building blocks that is enabling these societal changes. Over the next 10 years, I expect more such scenarios emerging." Here are the edited excerpts from the interview.
Rising potential
According to MarketsandMarkets Research Pvt. Ltd, the global digital twin market size is forecast to grow from $10.1 billion to $110.1 billion by 2023, driven by the increasing demand for digital twins in the healthcare industry and rising need for predictive maintenance. Major companies operating in this segment include General Electric, Siemens, Dassault Systemes, ABB, PTC, Robert Bosch, Microsoft and Amazon Web Services.
In 2022, the global digital twin market was valued at around $11.13 billion, according to Grand View Research, Inc. It is forecast to touch around $140 billion by 2030. The market is expected to get a boost with the widespread embrace of the Internet of Things (IoT) and big data analytics. Moreover, advancements in virtual reality and augmented reality are influencing the evolution of digital twin creation, contributing to the ongoing expansion of the market. Digital twins are being increasingly used to optimize and design workflows in supply chain processes, warehouse processes, smart city projects and production.
The TCS Digital Twindex forecasts that digital twins will become commonplace across business and society by 2035. Healthcare (52%), mobility (52%) and retail (47%) were selected by respondents as the industries that will adopt digital twins the most quickly, within the next three years. The study used the Delphi technique, aimed at reducing bias and arriving at a consensus around quantitative and qualitative questions.
Three years back, in November 2020, NTT Corporation (NTT) had proposed the concept of a bio-digital twin that allows users to map not only the brain but also the body and psychology. This September, NTT and the National Center for Neurology and Psychiatry (NCNP) announced a partnership to develop “Brain Bio-Digital Twin” technology with the help of AI and machine learning (ML).
According to the press release, the two companies will jointly focus on applying this technology to detect and prevent dementia, depression and other mental illnesses. NCNP, a board member of Japan Health Research Promotion Bureau, is building a library platform that aggregates and organizes vast amounts of data on mental and nervous system diseases that have been acquired through clinical and research activities. The idea is to model disease states using AI and ML and develop an AI brain simulator that predicts brain states and functions of disease states.
Source: The Concept of Brain Bio-Digital Twin (Photo: Business Wire)
The Brain Bio-Digital Twin will digitize different types of body data obtained through medical examinations and create detailed maps and biological models using the digital twin technology. As part of its collaboration with NTT, NCNP will provide image data such as PET (Positron Emission Tomography) and bio samples (blood, cerebrospinal fluid, tissue samples, genetic information etc.) "which are particularly useful for analysing cranial nerve diseases". NCNP will also medically interpret the connection between the outcomes derived from AI and ML processing and pathology, as well as the essential clinical implementation. The practical application of the Brain Bio-Digital Twin will enable the "twin" to be used for testing, rather than the patient's own brain and nerves.
But what about security and privacy concerns, especially since creating a digital twin of human organs involves getting data of human body parts?
Kevin Benedict, Futurist at TCS, acknowledges in the TCS Digital Twindex report that as digital twins become more integrated into our lives, "they could erode personal decision-making autonomy". He underscores that the data and algorithms used by digital twins need to be transparent, failing which it can lead to mistrust in their results. He recommends that regulations should enforce transparency requirements, including disclosing the data sources and algorithms used by digital twins. This, of course, is easier said than done.
Generative AI and digital twins
According to IBM, you can leverage generative AI for digital twin technologies in asset-intensive industries such as energy and utilities. For instance, by building a foundational model of utility asset classes such as towers, transformers and line, and by leveraging large scale visual images and adapting them to the client setup, IBM says it can use neural network architectures to scale the use of AI in identifying anomalies and damages on utility assets versus manually reviewing the image. Further, large-scale foundational models based on time series data and its co-relationship with work orders, event prediction, health scores, criticality index, user manuals and other unstructured data for anomaly detection, can be used to build models to create individual twins of assets which contain all the historical information accessible for current and future operation.
However, IBM, too acknowledges that generative AI and large language models (LLMs) "introduce new hazards to the field of AI, and we do not claim to have all the answers to the questions (such as bias, security, opacity, hallucination, and intellectual property) that these new solutions introduce". According to IBM, a majority of AI projects "get stuck in the proof of concept, for reasons ranging from misalignment to business strategy to mistrust in the model’s results".
IBM recommends that generative AI pilots should be driven by open technologies and that companies should ensure that AI is responsible and governed to create trust in the model, and empower those who use your platform.
WHAT ELSE IS HAPPENING AROUND THE WORLD?
OECD adopts new definition of AI to co-opt GenAI
In 2019, the Organisation for Economic Co-operation and Development’s (OECD) Council proposed a set of principles for trustworthy AI policies, which included an early definition of AI. This November, the Council adopted a new definition of AI that may soon be incorporated in the EU’s new AI rulebook. The new definition reads: “An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that [can] influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.”
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