The Embassy of Good Science - User contributions [en]

Theme:1855dccb-835d-419f-97df-f4cf82e4933f

2026-05-31T15:59:33Z

0009-0004-1660-8317:

{{Theme
|Theme Type=Good Practices
|Has Parent Theme=Theme:B2bce8c3-65f5-47d9-b6db-573b4b4f926f
|Title=Human Digital Twins in Healthcare: Ethical Challenges and Research Integrity Considerations
|Is About=A Human Digital Twin (HDT) is a virtual representation of an individual that integrates data from multiple sources, including medical imaging, genomic information, electronic health records, laboratory results, wearable devices, and physiological measurements. Using artificial intelligence and advanced computational models, digital twins can simulate biological processes, predict disease progression, and support personalized treatment decisions (1,2).

Human digital twins have attracted increasing interest in healthcare because they may improve diagnosis, optimize treatment planning, and facilitate precision medicine (2,3). However, the creation and use of digital twins raise important ethical and research integrity concerns regarding privacy, data ownership, informed consent, transparency, and accountability (1).<div></div>
|Important Because=Human digital twins rely on the continuous collection and integration of large volumes of highly sensitive personal data. This creates significant concerns regarding data security, confidentiality, and the potential misuse of health information (1). Unlike traditional medical records, digital twins may contain highly detailed representations of an individual's health status and future disease risks (1,3).

Another challenge concerns informed consent. Participants may consent to the use of their current data, but future applications of digital twin technologies may be difficult to predict. Researchers must therefore consider whether consent remains valid when new analytical methods or purposes emerge (1).

Questions of responsibility and accountability are equally important. If a treatment decision is influenced by a digital twin prediction that later proves incorrect, it may be unclear whether responsibility lies with clinicians, researchers, software developers, or healthcare institutions (1,4).<div></div>
|Important For=This topic is particularly relevant for researchers, clinicians, biomedical engineers, data scientists, ethics committee members, healthcare institutions, policymakers, and patients. It is especially important for investigators working in precision medicine, medical imaging, artificial intelligence, and digital health.
|Has Best Practice=Several research initiatives are currently exploring digital twin technologies in healthcare. In cardiology, digital twins have been used to simulate cardiac function and predict responses to different treatment strategies (2). In oncology, researchers are investigating whether digital twins can integrate imaging, genomic, and clinical data to predict tumor behavior and personalize cancer therapy (3).

Medical imaging plays a central role in many digital twin projects. Magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) provide detailed anatomical and functional information that can be incorporated into patient-specific computational models (2,3). These applications demonstrate the potential benefits of digital twins but also highlight concerns regarding data governance, transparency, and equitable access to technology (1,4).

As digital twin technologies continue to evolve, robust ethical oversight and responsible data management will be essential to ensure that innovation benefits patients while protecting their rights and interests (1,4).<div></div>References:

#Bruynseels K, Santoni de Sio F, van den Hoven J. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Front Genet. 2018;9:31.
#Corral-Acero J, Margara F, Marciniak M, et al. The 'Digital Twin' to Enable the Vision of Precision Cardiology. Eur Heart J. 2020;41(48):4556–4564.
#Björnsson B, Borrebaeck C, Elander N, et al. Digital Twins to Personalize Medicine. Genome Medicine. 2020;12:4.
#National Academy of Medicine. Artificial Intelligence and Digital Twins in Health Care: Opportunities and Ethical Challenges. Washington, DC; 2023.
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Theme:1855dccb-835d-419f-97df-f4cf82e4933f

2026-05-31T15:45:16Z

0009-0004-1660-8317:

{{Theme
|Theme Type=Good Practices
|Has Parent Theme=Theme:B2bce8c3-65f5-47d9-b6db-573b4b4f926f
|Title=Human Digital Twins in Healthcare: Ethical Challenges and Research Integrity Considerations
|Is About=A Human Digital Twin (HDT) is a virtual representation of an individual that integrates data from multiple sources, including medical imaging, genomic information, electronic health records, laboratory results, wearable devices, and physiological measurements. Using artificial intelligence and advanced computational models, digital twins can simulate biological processes, predict disease progression, and support personalized treatment decisions.

Human digital twins have attracted increasing interest in healthcare because they may improve diagnosis, optimize treatment planning, and facilitate precision medicine. However, the creation and use of digital twins raise important ethical and research integrity concerns regarding privacy, data ownership, informed consent, transparency and accountability.
|Important Because=Human digital twins rely on the continuous collection and integration of large volumes of highly sensitive personal data. This creates significant concerns regarding data security, confidentiality, and the potential misuse of health information. Unlike traditional medical records, digital twins may contain highly detailed representations of an individual's health status and future disease risks.

Another challenge concerns informed consent. Participants may consent to the use of their current data, but future applications of digital twin technologies may be difficult to predict. Researchers must therefore consider whether consent remains valid when new analytical methods or purposes emerge.

Questions of responsibility and accountability are equally important. If a treatment decision is influenced by a digital twin prediction that later proves incorrect, it may be unclear whether responsibility lies with clinicians, researchers, software developers, or healthcare institutions.
|Important For=This topic is particularly relevant for researchers, clinicians, biomedical engineers, data scientists, ethics committee members, healthcare institutions, policymakers, and patients. It is especially important for investigators working in precision medicine, medical imaging, artificial intelligence, and digital health.
|Has Best Practice=Several research initiatives are currently exploring digital twin technologies in healthcare. In cardiology, digital twins have been used to simulate cardiac function and predict responses to different treatment strategies. In oncology, researchers are investigating whether digital twins can integrate imaging, genomic, and clinical data to predict tumor behavior and personalize cancer therapy.

Medical imaging plays a central role in many digital twin projects. Magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) provide detailed anatomical and functional information that can be incorporated into patient-specific computational models. These applications demonstrate the potential benefits of digital twins but also highlight concerns regarding data governance, transparency, and equitable access to technology.

As digital twin technologies continue to evolve, robust ethical oversight and responsible data management will be essential to ensure that innovation benefits patients while protecting their rights and interests.

References:

#Bruynseels K, Santoni de Sio F, van den Hoven J. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Front Genet. 2018;9:31.
#Corral-Acero J, Margara F, Marciniak M, et al. The 'Digital Twin' to Enable the Vision of Precision Cardiology. Eur Heart J. 2020;41(48):4556–4564.
#Björnsson B, Borrebaeck C, Elander N, et al. Digital Twins to Personalize Medicine. Genome Medicine. 2020;12:4.
#National Academy of Medicine. Artificial Intelligence and Digital Twins in Health Care: Opportunities and Ethical Challenges. Washington, DC; 2023.
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Theme:1855dccb-835d-419f-97df-f4cf82e4933f

2026-05-31T15:41:35Z

0009-0004-1660-8317: Created page with "{{Theme |Theme Type=Good Practices |Has Parent Theme=Theme:B2bce8c3-65f5-47d9-b6db-573b4b4f926f |Title=Human Digital Twins in Healthcare: Ethical Challenges and Research Integ..."

{{Theme
|Theme Type=Good Practices
|Has Parent Theme=Theme:B2bce8c3-65f5-47d9-b6db-573b4b4f926f
|Title=Human Digital Twins in Healthcare: Ethical Challenges and Research Integrity Considerations
|Is About=A Human Digital Twin (HDT) is a virtual representation of an individual that integrates data from multiple sources, including medical imaging, genomic information, electronic health records, laboratory results, wearable devices, and physiological measurements. Using artificial intelligence and advanced computational models, digital twins can simulate biological processes, predict disease progression, and support personalized treatment decisions.

Human digital twins have attracted increasing interest in healthcare because they may improve diagnosis, optimize treatment planning, and facilitate precision medicine. However, the creation and use of digital twins raise important ethical and research integrity concerns regarding privacy, data ownership, informed consent, transparency and accountability.
|Important Because=Human digital twins rely on the continuous collection and integration of large volumes of highly sensitive personal data. This creates significant concerns regarding data security, confidentiality, and the potential misuse of health information. Unlike traditional medical records, digital twins may contain highly detailed representations of an individual's health status and future disease risks.

Another challenge concerns informed consent. Participants may consent to the use of their current data, but future applications of digital twin technologies may be difficult to predict. Researchers must therefore consider whether consent remains valid when new analytical methods or purposes emerge.

Questions of responsibility and accountability are equally important. If a treatment decision is influenced by a digital twin prediction that later proves incorrect, it may be unclear whether responsibility lies with clinicians, researchers, software developers, or healthcare institutions.
|Important For=This topic is particularly relevant for researchers, clinicians, biomedical engineers, data scientists, ethics committee members, healthcare institutions, policymakers, and patients. It is especially important for investigators working in precision medicine, medical imaging, artificial intelligence, and digital health.
|Has Best Practice=Several research initiatives are currently exploring digital twin technologies in healthcare. In cardiology, digital twins have been used to simulate cardiac function and predict responses to different treatment strategies. In oncology, researchers are investigating whether digital twins can integrate imaging, genomic, and clinical data to predict tumor behavior and personalize cancer therapy.

Medical imaging plays a central role in many digital twin projects. Magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) provide detailed anatomical and functional information that can be incorporated into patient-specific computational models. These applications demonstrate the potential benefits of digital twins but also highlight concerns regarding data governance, transparency, and equitable access to technology.

As digital twin technologies continue to evolve, robust ethical oversight and responsible data management will be essential to ensure that innovation benefits patients while protecting their rights and interests.

References:

# Bruynseels K, Santoni de Sio F, van den Hoven J. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Front Genet. 2018;9:31.
# Corral-Acero J, Margara F, Marciniak M, et al. The 'Digital Twin' to Enable the Vision of Precision Cardiology. Eur Heart J. 2020;41(48):4556–4564.
# Björnsson B, Borrebaeck C, Elander N, et al. Digital Twins to Personalize Medicine. Genome Medicine. 2020;12:4.
# National Academy of Medicine. Artificial Intelligence and Digital Twins in Health Care: Opportunities and Ethical Challenges. Washington, DC; 2023.
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User:0009-0004-1660-8317

2026-05-31T10:19:06Z

0009-0004-1660-8317: create user page