ArticleNature communications2024
Segment anything in medical images.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 686 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
686 citing papers in PubMed.
- Diagnostic accuracy and feasibility of artificial intelligence-driven smartphone imaging for dental caries detection: A systematic review.The Japanese dental science review · 2026Review
- Self-supervised small vessel segmentation with shape-aware geometric models and attention.Medical image analysis · 2026Article
- Improving deep learning organ segmentation accuracy in cone-beam CT-guided radiotherapy using a robust scatter suppression method.Physics and imaging in radiation oncology · 2026Article
- Fully automated diaphragm segmentation on routine 2D-cine and 3D thoracic MRI: multireader validation.European radiology experimental · 2026Article
- Automated thigh muscle segmentation using foundation models: validation and impact of segmentation quality.European radiology experimental · 2026Article
- CT-based multi-station lymph-node radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a proof-of-concept comparison with primary-tumor radiomics.European radiology experimental · 2026Article
- Adapting Segment Anything Model 3 for concept-driven lesion segmentation in medical images: an experimental study.Visual computing for industry, biomedicine, and art · 2026Article
- A Clinical Primer on Computer Vision.Calcified tissue international · 2026Review
- Robust cross-anatomy image registration leveraging keypoint priors and contour-based assessment.Medical & biological engineering & computing · 2026Article
- Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Pericoronary adipose tissue imaging: quantitative assessment, artificial intelligence integration, and therapeutic modulation.The British journal of radiology · 2026Review
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Article
- Instant3D: A Lightweight Open-Source GUI Frontend for TotalSegmentator-Based Medical Image Segmentation and 3D Reconstruction.Journal of imaging informatics in medicine · 2026Article
- Pseudotime uncovers restructuring dynamics and mitigators of metal catalyst deactivation.Science advances · 2026Article
- SOTS: Leveraging Self-supervised Pretraining for Label-Efficient Ovarian Tumor Segmentation in Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- Multi-Atlas-Based Segmentation of Pediatric Vocal Tract Anatomy in Dynamic Magnetic Resonance Imaging.Journal of speech, language, and hearing research : JSLHR · 2026Article
- NeuroRAD-FM: A Distributionally Robust Foundation Model for Precision Neuro-Oncology.Research square · 2026Article
- Computed Tomography-Derived Sarcopenia and Two- Versus Three-Dimensional Body Composition for Prognostication in Colorectal Cancer Patients Receiving Chemoradiotherapy: An Automated Deep Learning Segmentation Study with External Reproducibility Assessment.Healthcare (Basel, Switzerland) · 2026Article
626 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.
Funding
Abstract
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans.
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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.