Health Simulations: Ethical and Societal Challenges of Digital Twins
SIMTWIN aims to analyze the ethical and societal implications of Digital Twins in healthcare to develop a robust governance framework for their use in health simulations.
Projectdetails
Introduction
Digitisation has an impact on even the most fundamental concepts in medicine and public health, including our ideas of health and illness, embodiment, vulnerability, and controllability. Among the emerging technologies driving this paradigm shift is that of Digital Twins (DT), which presents exceptional challenges to healthcare governance, raising ethical and societal issues of which our understanding is still rudimentary.
Challenges of Digital Twins
DT may be empowering but could also exacerbate the vulnerability of both individual patients and the population at large. There are substantial gaps in our understanding of whether these new forms of artificial intelligence-driven health simulations provide new ways of engaging with experiences of human vulnerability, or whether they, in fact, introduce new forms of harm.
Project Overview
In this context, SIMTWIN will be the first project systematically identifying and examining the ethical and societal implications of the use of DT in healthcare. In doing so, SIMTWIN will promote our understanding of and practical approaches to new forms of simulation and prediction of health trajectories.
Objectives
SIMTWIN’s central objective is to provide a comprehensive and in-depth analysis of the normative challenges implied in order to develop an integrated theory of health simulations.
- Propose an empirically-based and normatively robust framework for an ethical and societal assessment of this technology.
- Enable the design of practical modes of controllability for the use of DT in health.
Significance
At a moment in history in which our societies find themselves facing crucial decisions on how to approach the complex issues raised by the dual, simultaneously transformative and disruptive character of health simulations, SIMTWIN promises ground-breaking insights into the associated normative and societal challenges and key orientations for a robust and innovative governance framework.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.497.275 |
Totale projectbegroting | € 1.497.275 |
Tijdlijn
Startdatum | 1-6-2023 |
Einddatum | 31-5-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONNpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
Digital twin politics: Unlocking the full potential of digital twins for sustainable ocean futuresTwinPolitics aims to analyze the socio-technical dynamics and political implications of developing digital twins of the ocean to enhance decision-making for sustainable ocean governance. | ERC Consolid... | € 1.999.734 | 2024 | Details |
Human collaboration with AI agents in national health governance: organizational circumstances under which data analysts and medical experts follow or deviate from AI.This project aims to explore the socio-cultural dynamics of AI in health governance across six countries to develop a theory on ethical AI intervention and its impact on national health policies. | ERC Starting... | € 1.499.961 | 2023 | Details |
Multiscale mechanobiological synergies in vascular homeostasis, ageing and rejuvenationJuvenTwin aims to revolutionize vascular ageing treatment by using multiscale digital twins to simulate and develop therapies targeting mechanobiological effects in aged arteries. | ERC Advanced... | € 2.795.438 | 2024 | Details |
Advanced Biopsychosocial Simulation for Harmful Adolescent BehaviourThe LIFECOURSE project aims to create a computational toolkit integrating biopsychosocial data to model and understand adolescent behaviors, enhancing research and societal impact. | ERC Proof of... | € 150.000 | 2022 | Details |
REinforcement TWInning SysTems: from collaborative digital twins to model-based reinforcement learningThe Re-Twist project aims to develop a novel Reinforcement Twinning framework that integrates machine learning with engineering to optimize systems like wind turbines and drones for societal benefits. | ERC Starting... | € 1.500.000 | 2025 | Details |
Digital twin politics: Unlocking the full potential of digital twins for sustainable ocean futures
TwinPolitics aims to analyze the socio-technical dynamics and political implications of developing digital twins of the ocean to enhance decision-making for sustainable ocean governance.
Human collaboration with AI agents in national health governance: organizational circumstances under which data analysts and medical experts follow or deviate from AI.
This project aims to explore the socio-cultural dynamics of AI in health governance across six countries to develop a theory on ethical AI intervention and its impact on national health policies.
Multiscale mechanobiological synergies in vascular homeostasis, ageing and rejuvenation
JuvenTwin aims to revolutionize vascular ageing treatment by using multiscale digital twins to simulate and develop therapies targeting mechanobiological effects in aged arteries.
Advanced Biopsychosocial Simulation for Harmful Adolescent Behaviour
The LIFECOURSE project aims to create a computational toolkit integrating biopsychosocial data to model and understand adolescent behaviors, enhancing research and societal impact.
REinforcement TWInning SysTems: from collaborative digital twins to model-based reinforcement learning
The Re-Twist project aims to develop a novel Reinforcement Twinning framework that integrates machine learning with engineering to optimize systems like wind turbines and drones for societal benefits.
Vergelijkbare projecten uit andere regelingen
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
InContract AIHet project onderzoekt de technische en commerciële mogelijkheden van digital twins voor het automatiseren van contractprocessen in de tool InContract, met inzet van AI en deep learning. | Mkb-innovati... | € 20.000 | 2023 | Details |
InContract AIHet project onderzoekt de inzet van digital twins en AI voor het automatiseren van contracten binnen de InContract-tool. | Mkb-innovati... | € 20.000 | 2023 | Details |
Haalbaarheidsonderzoek naar participatie door efficiënte Digital Twins.Het project onderzoekt de haalbaarheid van een innovatief digital twin-systeem voor burgerparticipatie, met als doel besluitvorming te verbeteren en commerciële toepassing te ontwikkelen. | Mkb-innovati... | € 20.000 | 2021 | Details |
EsperantoHet project ontwikkelt een software-oplossing die patiënten helpt hun gezondheid te monitoren en effectief te communiceren met zorgverleners, met real-time, meertalige feedback. | Mkb-innovati... | € 179.484 | 2015 | Details |
C-trappHet project ontwikkelt een applicatie die het vinden en aanmelden voor klinische studies vereenvoudigt, waardoor patiënten beter toegang krijgen tot experimentele zorg en onderzoekers sneller geschikte deelnemers vinden. | Mkb-innovati... | € 172.620 | 2015 | Details |
InContract AI
Het project onderzoekt de technische en commerciële mogelijkheden van digital twins voor het automatiseren van contractprocessen in de tool InContract, met inzet van AI en deep learning.
InContract AI
Het project onderzoekt de inzet van digital twins en AI voor het automatiseren van contracten binnen de InContract-tool.
Haalbaarheidsonderzoek naar participatie door efficiënte Digital Twins.
Het project onderzoekt de haalbaarheid van een innovatief digital twin-systeem voor burgerparticipatie, met als doel besluitvorming te verbeteren en commerciële toepassing te ontwikkelen.
Esperanto
Het project ontwikkelt een software-oplossing die patiënten helpt hun gezondheid te monitoren en effectief te communiceren met zorgverleners, met real-time, meertalige feedback.
C-trapp
Het project ontwikkelt een applicatie die het vinden en aanmelden voor klinische studies vereenvoudigt, waardoor patiënten beter toegang krijgen tot experimentele zorg en onderzoekers sneller geschikte deelnemers vinden.