ArticleFrontiers in genetics2022
You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Both ends of artificial intelligence impacting privacy: a review of violation and protection.Frontiers in artificial intelligence · 2026Pooled it
- Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward.JMIR public health and surveillance · 2026Article
- Cyberspatial privacy in the digital age: a proxemics-based framework with a healthcare application.BMC medical ethics · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Synthetic data generation of health and demographic surveillance systems data: a case study in a low- and middle-income country.JAMIA open · 2025Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Strengthening ethics review of the development of artificial intelligence (AI) systems in health research: a guide for research ethics committees in Uganda.BMC medical ethics · 2025Article
- Exploring Opportunities and Challenges of AI in Primary Healthcare: A Qualitative Study with Family Doctors in Lithuania.Healthcare (Basel, Switzerland) · 2025Article
- Article
- AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference.Clinical and translational science · 2025Review
- Review
- Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.Journal of general internal medicine · 2025Article
- How the world of biobanking is changing with artificial intelligence.Frontiers in digital health · 2025Review
- Quantitative evaluation and optimization of AI policy and regulatory texts for smart healthcare.Frontiers in public health · 2025Article
- The future of multimorbidity management in the older adults: transforming AI-enabled precision medicine.Frontiers in public health · 2025Review
- European health regulations reduce registry-based research.Health research policy and systems · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.Cureus · 2024Review
- Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine.BMJ health & care informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used "consent or anonymize approach" undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The "AI revolution" in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.
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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 OpenQuestion graph.