Another issue, she says, is reliance by some public sector hospitals on technologies dating back two generations that may not be able to connect with newer equipment and IoT sensors and sync seamlessly.

Innovations, however, are still being made in digital twin technology. Q Bio, a digital innovations company, recently launched its own digital twin platform, Q Bio Gemini. Powered by a whole-body scanner, it is the first of its kind designed for clinical use that can provide a comprehensive overview of a patient’s health in a scalable virtual model.


“In healthcare, the increasing power of computers and algorithms is enabling technologies to build a patient-specific digital twin, catering to our diversity as human beings and improving individual health outcomes,” said Mark Girolami, program director for data-centric engineering at The Alan Turing Institute, which was part of the study. “However, these promised advances are going to be hard won, requiring further concerted and sustained foundational research and development to fully realize the promise of the digital twin.”

Niederer says such efforts should include investments in high throughput data processing, data standards and automation of the manual process for creating twins. "The steps of identifying and processing relevant patient data and using this to create or update a digital twin will need to be automated by non-specialists to allow twins to be created at the scale of patient volumes in the clinic."

In addition to healthcare, the researchers also looked at the impact that digital twin technology could have on decision-making for aerospace systems.

The findings were published in Nature Computational Science.

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