ReviewNorth Carolina medical journal2024
Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias.
Review in North Carolina medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The evolving literature on the ethics of artificial intelligence for healthcare: a PRISMA scoping review.Frontiers in digital health · 2025Pooled it
- Integrating rapid response systems and patient centered goals of care discussions.Frontiers in medicine · 2026Article
- Artificial Intelligence and Bipolar Disorder: Applications of Machine Learning Models for Diagnosis, Treatment, and Outcome Prediction.Alpha psychiatry · 2025Article
- Personalized Treatment of Patients with Coronary Artery Disease: The Value and Limitations of Predictive Models.Journal of cardiovascular development and disease · 2025Review
- Advances in risk prediction models for cancer-related cognitive impairment.Clinical and experimental medicine · 2025Review
- The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.Antibiotics (Basel, Switzerland) · 2025Review
- Risk prediction for gastrointestinal bleeding in pediatric Henoch-Schönlein purpura using an interpretable transformer model.Frontiers in physiology · 2025Article
- Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead.North Carolina medical journal · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.
Indexed as
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.