My birth certificate tells me I am about 15 minutes older than my twin. Birth order has no universal relation to family rank or seniority. And so in some cultures I would be junior and he would be my senior – the second born sends the first to check the state of the world. And although these sorts of questions never much concern me I do get quizzed when people learn I have a twin. I am asked – which is older? Is he like you? Can I see a picture? When answering these questions I find myself speaking on behalf us, me and him, but also always a culture of twins and twinning, which is never quite what it seems.
During a recent research project I learned about the different criteria that are used to give twins shared or divergent identities. For example, one set of criteria assumes twins share conception, gestation, and birth. It is by sharing those experiences – if ‘experiences’ is the right word – that makes twins alike and yet different to non-twins. But advances made in molecular biology, fertility techniques and services, and the gradual development of genome-editing technologies make these assumptions increasingly visible and potentially fragile. Twins are born years apart, using cryogenic IVF techniques, and the world’s first known germline genome editing experiment created twin children. If twins continue to be twins without a common conception, gestation, or birth then what is it that forms their union? Throughout my life single-born people have told me they wish they had a twin of their own. But I wonder what they mean and if they have a particular kind of twin sibling in mind – agreeable and kind, with whom they ‘share’ a lot. I guess that’s one nice thing about having an imaginary twin: you get to choose and they will rarely disagree.
Despite evident cultural and historical differences about what defines a twin, there exists a powerful cultural idea that twins should be alike. This is partially registered and reproduced in the creation of ‘digital twins’ – which, according to one definition, involve the virtual representations of real-world entities and processes, and the mechanism by which they are synchronized to correspond to one another. Digital twins are real-time models of existing objects or persons. Techniques of simulated modelling originates in space engineering, specifically NASA’s Apollo programme, but the term has become widely adopted and digital twins are now built for power stations, manufacturing processes, historic buildings, and whole cities can have digital twins for emergency planning.
The development of digital twins in healthcare is closely linked to personalised medicine. Rhetorically, they are used to propose a near-perfect data double, ‘virtual patient’ or ‘in-silico-self’, used as a kind of shadow to continuously track and provide predictions about health and disease conditions for individual patients. The reality is more partial and complicated, since digital twins must be built and tested against other digital twins and their data sources. Though the word ‘twin’ may suggest uniformity to some the creation of ‘digital twins’ within programmes of machine learning and artificial intelligence mean the field is more unstable, rapidly changing and adaptive – data collection and analytics techniques are varied, and the data used made of aggregate patients and selves rather than the tracked data of individual persons. The ‘what-if’ scenarios said to be the digital twin’s strength also depend on the ‘what-if’ of innovation platforms, their changing data techniques, investment patterns, and industry standards.
Discrete areas of progress reveals how digital twins perform as a patchwork aggregate of different people’s data rather than a 1-for-1 representation of legal individuals. For example, in 2019 Dassault Systèmes’ announced its collaboration with the U.S. Food and Drug Administration to develop its digital twin called Living Heart, a simulated 3D heart model. These single-organ digital twins promise to enhance training, testing, clinical diagnosis and regulation – with particular benefits for safer, quicker, less expensive clinical trials. While the idea of a ‘twin’ suggests someone or something in parallel or in partnership with one other person – rather than many – Dassault Systèmes’ data is simulated, modelled, or imputed from existing patients. It’s a model heart made after the hearts of many.
Just as jet engines now carry hundreds of sensors that track, model, and predict engine behaviour, digital twin developers envision the use of wearables, such as watches, socks, implanted and ingestible devices, which can gather situated data and give shape to a person’s digital twin. While the ‘virtual’ status of a digital twin – which one might assume to be a faithful and precise representation of a living person – is core to its advantage and potential use, the promise of precision requires extensive material resources. While the twin is viewed as digitally adaptive, updated, ‘smart’ – the sources of data must be standardised and compliant to stream updates according to specific timelines. The liveliness of the digital twin depends on its real life
partner doing a lot of legwork. Sampling frequencies may be ‘continuous’ in theory but twin updates are scheduled or serialised over discrete time points, according to existing or emerging protocols that set trends and shape forecasts. Tracked data, already partial, tracks persons according to a schedule governed by standardised routines. Patients with multiple health and disease conditions will continue to follow variegated collection and update routines, such that arrhythmias of the heart are likely to require close monitoring but early cancer detection is unlikely to follow the same ‘real-time’. In this sense the digital twin will rarely if ever be contemporary to the multiple datasets it makes interoperable. Nor will it perfectly replicate the subject it is said to model. In practice digital twins are more like Frankenstein’s creature – time-lapsed kin made of composite portraits, layering different data types updated at different times. And like all twins who are physically born in the UK National Health Service, existing standards of classification, regulation and governance will mediate between and differentiate one twin and another.
Although digital twins are often promoted as a way to automate and simplify how people’s future health is figured, enduring questions about how orders of the normal and the pathological shape group affinities and differences. These are questions also asked of a ‘personalised’ medicine more generally. Though the digital twin may appear ‘personal’ to its real-world kin – it is another ‘you’ yet paradoxically uniquely ‘yours’ – the accuracy and validation of a twin-made predictions will depend on it being a generic composite. Hence, the what-if implications of implementing digital twins are very different depending on context. On a factory floor, simulations of a machinery slowing or malfunctioning can provide real-time analytics to help forecast ‘what-if’ changes in a supply chain. If a clinician explained that your digital twin’s simulated response in a drug trial led to an adverse reaction, would you accept their recommendation that you begin palliative care? On the other hand, a digital twin’s simulated status means they can be coached – a figure to improve and enhance, a figure to discuss among friends (which is older? Are they like you? Can I see a picture?). What remains uncertain is whether there will emerge vernacular ways to involve others when describing your twin, a way of recognising your self in aggregated and simulated collectives, a we, a culture of digital twins and twinning assumed to be alike but always a little different.