The use of digital twins in healthcare is rapidly increasing, and one application area is the personalisation of medical care, where digital twins can take the shape of in silico models of organs and organ systems used to test various treatment options, to customise therapy or plan surgery.
Also in the development of medical therapies (drugs & devices), digital twins can be used as a tool throughout the entire R&D process to identify knowledge gaps and flaws, obtain a holistic and better understanding of a patient's disease, design novel strategies, optimise therapies, optimise therapy production, increase safety and shorten the time to market.
A wide range of definitions of the concept of digital twins exist, mostly varying in the requirements on the nature of the coupling between the physical entity and its digital counterpart. If, in general, digital twins are thus believed to imply a continuous and real-time interrelation between the digital and physical world, in healthcare, however, a more specific definition is usually employed regarding digital twins of systems in the living world, in the sense that this coupling does not always need to be a real-time one.
Until now, current digital twin solutions for healthcare have been mostly single-scale, single-organ, single disease systems, given the fact that going beyond this appeared to be highly complicated and time-consuming. More recently, however, ambitious goals have started to surface, aiming at the vision of a validated, integrated multi-scale, -time and -discipline digital twin of the whole body, enabling the comprehensive characterization of the physiological and the pathological state in its full complexity and heterogeneity. This translates into the prospective need of some distributed digital infrastructure, made of data and model standards, standard operative procedures (SOPs), and standardised application programming interfaces, that will allow researchers and developers of digital twins to accrue not only predictive models, but possibly also the experimental and clinical data employed in building and validating these models, into some shared facility for reuse and collaboration.
Following-up on previous sessions focusing on health data sharing issues and privacy-enhancing technologies, the HDIG proposes to dedicate its meeting in Seoul at P19 to exploring the usage and the outlook of digital twins in healthcare, ensuring inclusiveness, fairness, and transparency.
Collaborative session notes: https://docs.google.com/document/d/1mIDctIGcXj9paRIfLNlSheQyxl0TAabyaoPc...
- Short introduction to the HDIG activities
- Presentations on digital twins and health data/models issues and perspectives
- Q&A and discussion on topics presented
- Next steps
For this open session, we invite Policymakers for Healthcare; Clinicians wanting to use data technology to improve their practice; Biomedical researchers using data-driven analytical techniques in their research life-cycle; Healthcare Data Scientists dealing with data mining, machine learning and AI, natural language processing, physiological modelling and image processing technologies and the data these produce; Health bioinformatics legal experts; Healthcare and Health Maintenance Organisation administrators; Pharmaceutical industry researchers and manufacturers; Medical equipment researchers and manufacturers, in silico modelling, testing and clinical trial experts; and, participants form other related WG/IG.
When the HDIG was officially instituted, in 2016, it was the only RDA group focusing on the intricacies of Health Data, especially as it relates to privacy and security issues in Healthcare but not only. The novelty and importance of the issues brought forward was such, that the Health Data IG sessions have become a moment where new topics which are gaining rising interest for scientific research communities – such as the entry into force in May 2018 of the EU General Data Protection Regulation (GDPR) or Artificial Intelligence applied to Health – can be debated within a wider and competent audience and with a worldwide perspective.
Up to now, many new groups are starting to explore different aspects related to Health Data, and in particular two working groups (WG) spread from the HDIG, one on “Blockchain Applications in Health” and another one on “Reproducible Health Data Services”.
The Blockchain Applications in Health WG concluded its 18-month-activities delivering a final Report on ‘Comprehensive Guidelines on Blockchain Applications in Health’, which was presented and discussed during the last Blockchain Applications in Health WG’s session at P17.
The Health Data Interest Group (HDIG) was officially instituted in 2016 following successful BoF Sessions during the 6th RDA Plenary Meeting in Paris and the 7th RDA Plenary Meeting in Tokyo. It is now a mature RDA component, actively involved in the 8th RDA Plenary Meeting in Denver with a session titled “Health Data Privacy & Security issues”, at P9 in Barcelona with a session focused on “Meaningful health data for research and for industry” at P10 in Montreal with a session on “Health data mapping and diverging trends in health data protection”, at P11 in Berlin on “First results on RDA Adoption and Training Guide for Reproducible Data Service Workflows and diverging trends in Health data protection” and at P12 in Gaborone, Botswana, where the topic of “Genomic data in the light of privacy rules” was addressed. The meetings in Philadelphia (P13) and Helsinki (P14) were the occasion for HDIG members to discuss more in detail and on a use-case basis the theme of Artificial Intelligence, with special regards to “AI medicine: preconditions to apply AI to medicine and privacy concerns (after the EU GDPR)” and “Hospitals’ experiences towards a large-scale data sharing ecosystem for AI”.
Keeping on working on these topics, even if facing some difficulties in attending all plenaries due to the global pandemic spreading out in 2020, HDIG’s session at VP16 in Costa Rica (virtual) was dedicated to “Transparency and Trust in Health Data”, while during P17 in Edinburgh (virtual) the session titled “Achieving anonymity and correcting bias with synthetic data through generative AI” focused on facing the advent of synthetic data.