ArticleComputational and structural biotechnology journal2025
Synthetic data in medicine: Legal and ethical considerations for patient profiling.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026Guideline
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Synthetic Medical Data vs. The Four Principles: Ethical Trade-Offs and Zombie Data.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Review
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- Dentists' awareness of data security and ethical issues during the transition to artificial intelligence-driven clinical practice.Acta odontologica Scandinavica · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Progress in educational theory and translational implementation of simulation-based education for healthcare professionals: an integrative analysis from theory to effectiveness.Frontiers in medicine · 2026Review
- Opportunities and challenges in automated coding of electronic health records: a pilot study for rare disease registries.Frontiers in digital health · 2026Article
- A call for principle-based acceptance of synthetic patients: establishing the AI-driven foundation for regulatory submissions.Frontiers in digital health · 2026Review
- From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- On the use of synthetic data for healthcare AI in Africa: Technical performance, governance challenges, and policy readiness.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic data is increasingly used in healthcare to facilitate privacy-preserving research, algorithm training, and patient profiling. By mimicking the statistical properties of real data without exposing identifiable information, synthetic data promises to resolve tensions between innovation and data protection. However, its legal and ethical implications remain insufficiently examined, particularly within the European Union (EU) regulatory landscape. This paper contributes to the emerging field of synthetic data governance by proposing a differentiated legal-ethical framework tailored to EU law. This paper follows a three-part taxonomy of synthetic data (fully synthetic, partially synthetic, and hybrid synthetic data) based on generation methods and identifiability risk. This taxonomy is situated within the broader context of the General Data Protection Regulation, the Artificial Intelligence Act, and the Medical Devices Regulation, clarifying when and how synthetic data may fall under EU regulatory scope. Focusing on patient profiling as a high-risk use case, the paper shows that while fully synthetic data may not constitute personal data, its downstream application in clinical or decision-making systems can still raise fairness, bias, and accountability concerns. The ethical analysis of profiling practices utilizing synthetic data is conducted through the lens of the four foundational biomedical principles: autonomy, beneficence, non-maleficence, and justice. The paper calls for sector-specific standards, generation quality benchmarks, and governance mechanisms aligning technical innovation with legal compliance and ethical integrity in digital health.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.