Aug 2026· Health Care Analysis· 0 citations· 34 references
Medicine
TL;DR
It is shown that Transparency, privacy, scalability, and fidelity are all linked in complex ways with the principles of medical ethics and it is shown that when privacy is privileged over other values, it conflicts with autonomy and beneficence.
Abstract
Rapid advances in AI for medical applications are accompanied by an increasing demand for vast datasets. Privacy constraints - legal restrictions such as GDPR, as well as more general ethical concerns - pose significant barriers to the acquisition and use of data. Synthetic medical data (SMD) is AI-generated data that mimics real patient information. SMD is being promoted as an ethical solution that preserves privacy while enabling scalable model training. However, as we will show, the use of SMD raises its own set of issues. SMD's emphasis on privacy and scalability subtly embeds ethical and epistemic trade-offs that undermine the principles commonly regarded as being paramount in medical ethics. These trade-offs lack the kind of ethical justification one would expect in decisions that disrupt commonly accepted values and priorities, such as those captured by Beauchamp and Childress' four principles. In the discourse on the ethical advantages of SMD, privacy tends to be treated as a value in its own right. We show that this is a problematic assumption. Transparency, privacy, scalability, and fidelity are all linked in complex ways with the principles of medical ethics. We map these relationships and show that when privacy is privileged over other values, it conflicts with autonomy and beneficence. Our aim here is not to establish that this privileging is wrong per se, but to show that it needs careful analysis before we can accept the idea that SMD is indeed ethically advantageous.
Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
Ze-Yang Zhu, Matthias N. Louws, Roland V. Bumbuc et al.· 0 citations
This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Jamila Alsayed Kassem, Tim Müller, Christopher A. Esterhuyse et al.· 0 citations
This paper examines how privacy-enhancing technologies such as synthetic data, federated learning and ‘Secure Data Environments’ can be integrated into artificial intelligence (
AI
) development processes to uphold key data protection principles in the UK
GDPR
, like storage limitation, data minimisation, purpose limitation, security, and fairness. The analysis highlights how privacy-enhancing technologies offer benefits beyond anonymisation by embedding privacy-by-design values to support responsible innovation and protect sensitive patient data throughout the design, training, and validation of medical
AI
systems. The paper uses the 2015 DeepMind and Royal Free case as a practical study to realise the practical and legal benefits of privacy-enhancing technologies in medical
AI
development, particularly involving public-private collaborations. While grounded in the UK context, the findings have broader relevance to the European Union and other international jurisdictions grappling with tensions between data protection and
AI
development in the healthcare context.
Yasmine Zoya· European Journal of Health L...· 0 citations
Researchers in resource-constrained settings continue to face ethical challenges in collecting, using, and sharing data within Trusted Research and Data Environments (TRDEs). While open science promotes transparency, equity, and collaboration, it also heightens privacy concerns amid the rapid spread of artificial intelligence, big data, and machine learning. The African Population and Health Research Center (APHRC) is leading efforts to strengthen ethical data governance in Africa, focusing on how to protect privacy without restricting fair access to data. Growing risks such as data protectionism and restrictive monetization threaten cross-border collaboration and deepen inequalities across low- and middle-income countries (LMICs). APHRC’s approach balances individual rights with collective benefits by treating privacy as both a safeguard and an enabler of open science. Guided by the principle that data is a public good, the Center aligns global frameworks—such as the AU Data Policy Framework, GDPR, and UNESCO’s Open Science Recommendation, with regional instruments and practical innovations. These include the APHRC Central Data Catalogue, a secure repository governed by the Data Sharing Policy; improved metadata systems aligned with FAIR principles; a Data Governance Curriculum on the Virtual Learning Academy; the DASSA Platform supporting federated learning; and a low-code anonymization tool that simplifies privacy practices. Through these initiatives, APHRC demonstrates enhanced data visibility, capacity building, and trust while embedding methods such as k-anonymization, differential privacy and federated learning. This experience shows that context-sensitive governance rooted in CARE and FAIR principles can advance ethical, inclusive, and globally interoperable open science.
B. Ingumba· International Journal of Pop...· 0 citations