ReviewNPJ digital medicine2025
Addressing contemporary threats in anonymised healthcare data using privacy engineering.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Digital Nutritional Care in Oncology: Opportunities to Enhance Quality of Life and Support Patient-Centered Care.Cancers · 2026Review
- A temporally Anchored Retrieval-Augmented Generation Framework for Metabolic and Bariatric Surgery Patient Education: An IFSO Artificial Intelligence Task Force Multinational Validation Study.Obesity surgery · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- K-anonymity decay in multi-turn clinical large language model conversations.Frontiers in digital health · 2026Article
- Access to digital health technologies: personalized framework and global perspectives.Nature reviews. Cardiology · 2026Review
- Regulating algorithmic tools in reproductive health: ethical and legal challenges.Frontiers in reproductive health · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Navigating the tradeoff between personal privacy and data utility in speech anonymization for clinical research.NPJ digital medicine · 2025Review
- Challenges and opportunities for the next generation of computational tumor models.PLoS biology · 2025Article
- From data-driven cities to data-driven tumors: dynamic digital twins for adaptive oncology.Frontiers in artificial intelligence · 2025Article
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
Abstract
Cyber-attacks on healthcare entities and leaks of personal identifiable information (PII) are a growing threat. However, it is now possible to learn sensitive characteristics of an individual without PII, by combining advances in artificial intelligence, analytics, and online repositories. We discuss privacy threats and privacy engineering solutions, emphasizing the selection of privacy enhancing technologies for various healthcare cases. Future solutions must consider dynamic flows of data throughout their lifecycle.
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.