ReviewNature biomedical engineering2022
Self-supervised learning in medicine and healthcare.
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 209 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
209 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Optimizing metabolic health through deep-phenotyping-based personalized nutrition.Nature reviews. Endocrinology · 2026Review
- MRI-based fetal gestational age estimation using a structure-aware self-supervised network.European radiology · 2026Article
- Resolution-dependent self-supervised transfer in chest radiograph classification.Communications medicine · 2026Article
- Hierarchical contrastive learning from GP-recorded and eGFR labels for CKD staging.Medical & biological engineering & computing · 2026Article
- Mental health monitoring in the workplace using artificial intelligence: A scoping review.PLOS digital health · 2026Article
- From generalization to precision: A large domain-specific pretrained model for specialized medical tasks.Cell reports. Medicine · 2026Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Article
- Development and temporal validation of a machine learning-based model for predicting renal impairment in multiple myeloma: a single-center retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- Halo-free quantitative phase imaging via physics-constrained self-supervised network.Journal of biomedical optics · 2026Article
- Sleep EEG foundation models reveal within-stage microstructure that improves health screening beyond traditional stages.Research square · 2026Article
- Towards clinical-level interpretation of dental panoramic radiography using an instance-guided vision-language model.Nature biomedical engineering · 2026Article
- Machine Learning Across Heterogeneous Biomedical Data: Representation, Integration, and Deployable Systems.Bioengineering (Basel, Switzerland) · 2026Review
- A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.BMC oral health · 2026Article
- Enhancing foundation model transfer for prostate cancer detection with patch-level contrastive learning.NPJ digital medicine · 2026Article
- Universal and transferable attacks on pathology foundation models using microscopic perturbations.Light, science & applications · 2026Article
149 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
3 authors.
Funding
Abstract
The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, we highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins. We also discuss promising applications of self-supervised learning for the development of models leveraging multimodal datasets, and the challenges in collecting unbiased data for their training. Self-supervised learning may accelerate the development of medical artificial intelligence.
Indexed as
Identifiers
35953649What 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.