Healthcare’s digital twin ambitions clash with ethics, law, and social trust

Despite heavy investment and rising policy interest, healthcare digital twins remain largely confined to experimental pilots rather than routine clinical use. A new academic review finds that the main obstacles are not technological shortcomings, but unresolved ethical, legal, and social constraints embedded in healthcare systems themselves.

The study, Realising the Digital Twin: A Thematic Review and Analysis of the Ethical, Legal, and Social Issues for Digital Twins in Healthcare, published in AI & Society, analyzes why digital twin technologies have struggled to scale across healthcare settings.

The promise of digital twins meets structural reality

Digital twins in healthcare are designed as continuously updated virtual representations that integrate data from electronic health records, medical imaging, genomics, wearables, and real-time monitoring devices. In theory, these systems allow clinicians to simulate treatment options, forecast disease progression, and tailor interventions to individual patients rather than population averages. At the system level, digital twins are also promoted as tools to optimize hospital operations, manage patient flow, and improve emergency preparedness.

The review finds that these ambitions have outpaced empirical validation. While operational digital twins used for hospital logistics have demonstrated measurable efficiency gains, evidence supporting patient-specific digital twins remains limited. Validation standards for clinical accuracy are inconsistent, and many claims about long-term cost savings or preventive benefits remain speculative.

The authors identify a recurring pattern across the literature: digital twins are frequently described as transformative technologies without sufficient attention to the conditions required for safe, equitable, and effective deployment. Data integration challenges, infrastructure constraints, and workforce readiness issues persist, particularly in under-resourced healthcare systems. As a result, digital twins risk reinforcing existing inequalities rather than democratizing access to advanced care.

Ethical concerns emerge as a key fault line. Digital twins depend on continuous and highly granular data collection, raising serious questions about privacy, consent, and control. Unlike traditional medical records, digital twins aggregate behavioral, physiological, and environmental data over time, creating comprehensive digital profiles that may be difficult for patients to fully understand or manage. The review highlights fears that such data could be repurposed for commercial gain, insurance discrimination, or surveillance-oriented health management.

Equity is another major concern. The infrastructure and expertise required to develop and maintain digital twins are concentrated in well-funded institutions, creating a divide between healthcare systems that can afford these technologies and those that cannot. Populations with the greatest health needs, including rural communities and marginalized groups, are often the least likely to benefit from advanced digital twin applications. The study warns that without deliberate policy intervention, digital twins could deepen existing disparities in access to precision medicine.

Regulatory, legal, and governance gaps

The review identifies legal uncertainty as a significant obstacle to adoption. Existing regulatory frameworks were largely designed for static medical devices or episodic clinical decision tools, not continuously learning systems that evolve over-time. Digital twins blur traditional boundaries between medical devices, software, and clinical judgment, complicating approval pathways and post-market oversight.

Data protection laws such as the General Data Protection Regulation (GDPR) offer safeguards against automated decision-making in healthcare, but the review notes that these frameworks struggle to accommodate the scale and complexity of digital twin data flows. Continuous monitoring, cross-institutional data sharing, and real-time model updates introduce governance challenges that current consent and accountability mechanisms were not designed to address.

Ownership of digital twin data and outputs remains unresolved. The review highlights growing concern over who controls the value generated by digital twins, particularly when patient data contributes to commercially valuable models. While patients are the source of much of the underlying data, ownership rights often default to healthcare institutions or technology companies. This imbalance raises questions about benefit sharing, transparency, and trust.

Liability is another unresolved issue identified in the study. When clinical decisions informed by digital twins lead to harm, it is unclear whether responsibility lies with clinicians, developers, data providers, or healthcare organizations. The distributed and adaptive nature of digital twin systems complicates traditional malpractice frameworks and may discourage clinicians from relying on these tools in high-stakes settings.

The study also points to fragmentation across jurisdictions. Differences in data governance rules, regulatory standards, and cross-border data transfer requirements limit the scalability of digital twins and constrain international collaboration. Rather than enabling global health innovation, these inconsistencies risk confining digital twin development within national or institutional silos.

Adoption barriers and the future of trust in healthcare AI

Social and organizational factors play a decisive role in whether digital twins succeed or fail. The review emphasizes that healthcare is a sociotechnical system, where technologies must align with professional practices, institutional cultures, and patient expectations. Digital twins introduce new forms of cognitive and operational burden, requiring clinicians to interpret probabilistic outputs, manage continuous data streams, and negotiate uncertainty in ways that differ from traditional care models.

Human factors, including trust, usability, and workflow integration, are repeatedly identified as underappreciated barriers. Clinicians may resist recommendations that conflict with experiential knowledge, while patients may view continuous monitoring as intrusive or anxiety-inducing. Without careful design and communication, digital twins risk shifting attention away from embodied patient care toward abstract model outputs.

Public trust emerges as a critical condition for long-term sustainability. The review warns that over-reliance on algorithmic systems could erode confidence in human clinical judgment, while high-profile data breaches or misuse of health data could undermine social acceptance. These risks are amplified by broader societal concerns about artificial intelligence, surveillance, and corporate control of personal data.

To explain why many digital twin initiatives stall at the pilot stage, the authors apply the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability framework. This approach highlights how complexity across clinical conditions, technologies, value propositions, adopters, organizations, and regulatory systems can overwhelm even promising innovations. The analysis shows that ethical, legal, and social issues are not peripheral obstacles but core determinants of whether digital twins can move from experimentation to routine use.