ArticleScience and engineering ethics2025
Big Data, Machine Learning, and Personalization in Health Systems: Ethical Issues and Emerging Trade-Offs.
Article in Science and engineering ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Wearable Technology and Sensors for Healthcare and Wellbeing.Sensors (Basel, Switzerland) · 2026Article
- Analysis of the anti-scalping mechanism of hospital appointment registration based on Bayesian theory.Frontiers in public health · 2026Article
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
The use of big data and machine learning has been discussed in an expanding literature, detailing concerns on ethical issues and societal implications. In this paper we focus on big data and machine learning in the context of health systems and with the specific purpose of personalization. Whilst personalization is considered very promising in this context, by focusing on concrete uses of personalized models for glucose monitoring and anomaly detection we identify issues that emerge with personalized models and show that personalization is not necessarily nor always a positive development. We argue that there is a new problem of trade-offs between the expected benefits of personalization and new and exacerbated issues - results that have serious implications for strategies of mitigation and ethical concerns on big data and machine learning.
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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.