ArticleNPJ digital medicine2025
Embedded framework for clinical medical image segment anything in resource limited healthcare regions.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for pathologic myopia classification based on the META-PM system: a systematic review and meta-analysis.Frontiers in physiology · 2026Pooled it
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Based Histopathology Segmentation for Resource-Constrained Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of portable imaging devices improves medical image acquisition efficiency in resource-limited regions, but a shortage of medical personnel still limits timely diagnosis. We propose Embed-MedSAM, a fully automatic segmentation model with low deployment cost. Built on MedSAM, it integrates a lightweight RepViT encoder to reduce computation and applies two-stage distillation on over one million multimodal medical images to preserve the original model's visual representation. A self-prompting mechanism is also introduced, where the model generates pseudo labels to guide fine-grained segmentation. The training jointly optimizes KL divergence and segmentation losses to improve accuracy under prompt-free conditions. Embed-MedSAM shows excellent performance on 17 benchmark datasets covering 7 imaging modalities. Without external prompts, it improves average Dice score by nearly 16% over the second-best model. It also runs at nearly 30 FPS on iPhone 14, showing strong potential for real-world deployment.
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.