ArticleNature biomedical engineering2022
Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning.
Article 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 163 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
163 citing papers in PubMed, 352 citations in OpenAlex.
- Language-supervised electrocardiogram interpretation with vision transformer.Health information science and systems · 2026Article
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- A Multitask Deep Learning Model for Pediatric Echocardiography Analysis.Circulation · 2026Article
- Annotation-Efficient Vulnerable Carotid Plaque Identification in 3D MRI: A Multimodal Knowledge-Transferred Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative Study of CNN, Hybrid, and Transformer Architectures in Medical Image Classification.Journal of imaging informatics in medicine · 2026Article
- Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning.Nature communications · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- A multimodal vision-language model for generalizable annotation-free pathology localization.Nature biomedical engineering · 2026Article
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- Chest-X-ray based deep feature-driven framework for anemia detection insights from a large hospital cohort.BioData mining · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Transparent chest radiograph foundation model enables explainable human disease profiling.NPJ digital medicine · 2026Article
- Article
- Encoding Versus Linear Use of Patient Characteristics in Chest X-Ray Foundation Models on MIMIC-CXR.Diagnostics (Basel, Switzerland) · 2026Article
- Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- Learning like a radiologist: a medical vision-language model for radiological image analysis via curriculum learning.NPJ digital medicine · 2026Article
103 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
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed the performance of medical experts. Yet such a high-level of performance typically requires that the models be trained with relevant datasets that have been painstakingly annotated by experts. Here we show that a self-supervised model trained on chest X-ray images that lack explicit annotations performs pathology-classification tasks with accuracies comparable to those of radiologists. On an external validation dataset of chest X-rays, the self-supervised model outperformed a fully supervised model in the detection of three pathologies (out of eight), and the performance generalized to pathologies that were not explicitly annotated for model training, to multiple image-interpretation tasks and to datasets from multiple institutions.
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.