ReviewPLOS digital health2022
Global healthcare fairness: We should be sharing more, not less, data.
Review in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis 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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Between opportunity and equity: navigating medical tourism in Türkiye's public hospitals.BMC health services research · 2026Article
- State of abdominal CT datasets: A critical review of bias, clinical relevance, and real-world applicability.PLOS digital health · 2026Review
- Evaluating the quality of tabular synthetic data in health care.PLOS digital health · 2026Article
- Data fairness-the new social challenge of the data economy: what shapes social media users' perception of data fairness?Frontiers in psychology · 2026Article
- Connecting biospecimens and data: a scoping review-informed conceptual framework for digital infrastructure in biobanking and genomic research in low- and middle-income countries.Frontiers in public health · 2026Article
- Digitalisation of medicine in LMICs (sub-Saharan Africa): enablers, barriers, and funding strategies for sustainable health systems.Frontiers in public health · 2026Review
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine.Circulation. Genomic and precision medicine · 2025Review
- A quantitative analysis of the use of anonymization in biomedical research.NPJ digital medicine · 2025Article
- Addressing contemporary threats in anonymised healthcare data using privacy engineering.NPJ digital medicine · 2025Review
- Enhancing bronchopulmonary dysplasia prediction in preterm infants using artificial intelligence and multimodal data integration.Frontiers in pediatrics · 2025Review
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Guardians of the data: NER and LLMs for effective medical record anonymization in Brazilian Portuguese.Frontiers in public health · 2025Article
- Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool.Scientific reports · 2024Article
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- Data as scientific currency: Challenges experienced by researchers with sharing health data in sub-Saharan Africa.PLOS digital health · 2024Article
- The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain.PLOS digital health · 2024Article
- Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing.PLOS digital health · 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
11 authors.
Funding
Abstract
The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost-measured in terms of access to future medical innovations and clinical software-of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence's progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur-for the benefit of a global medical knowledge system.
Identifiers
What OpenQuestion 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 OpenQuestion graph.