ReviewNature biomedical engineering2022
Shifting machine learning for healthcare from development to deployment and from models to data.
Review in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 7 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
165 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review.Cancer reports (Hoboken, N.J.) · 2025Pooled it
- The application of machine learning in predicting post-cardiac surgery acute kidney injury in pediatric patients: a systematic review.Frontiers in pediatrics · 2025Pooled it
- Impact of Natural Language Processing models on diagnosis and decision-making in healthcare, business, education, and sports: a review.Frontiers in artificial intelligence · 2025Pooled it
- Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.Current hypertension reports · 2024Pooled it
- Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.Cell reports. Medicine · 2024Pooled it
- Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.Alzheimer's research & therapy · 2023Pooled it
- Cervical cancer survival prediction by machine learning algorithms: a systematic review.BMC cancer · 2023Pooled it
- Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care.Bioengineering (Basel, Switzerland) · 2026Article
- Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Attitudes and Usage of ChatGPT Among Medical and Paramedical Students in Iran: A Cross-Sectional Study.Health science reports · 2026Article
- Surgical data extractivism: the false promise of institutional custodianship.Hepatobiliary surgery and nutrition · 2026Article
- Validating medical digital twins for clinical decision support: beyond predictive accuracy.JAMIA open · 2026Review
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Machine learning-based clinical models for prediction of urinary incontinence after robot-assisted laparoscopic radical prostatectomy.BMC surgery · 2026Article
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Machine learning approaches to predict maternal anemia and Identifying associated factors among pregnant women in Sub-Saharan Africa.Journal of public health research · 2026Article
- An Attention-Enhanced ViT-HLNN Hybrid Ensemble Framework for Multi-Class Gastrointestinal Disease Classification.Scientific reports · 2026Article
- An artificial cilia-based array system for sound frequency decoding and resonance-responsive drug release.Nature biomedical engineering · 2026Article
- The limits of debiased clinical language models for cross-hospital generalization.Scientific reports · 2026Article
- A Review of Data Engineering in United States Healthcare Infrastructure.Healthcare (Basel, Switzerland) · 2026Review
105 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.
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.