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Background <p>The Medical Segment Anything Model (MedSAM) has emerged as a prominent foundation model for medical image segmentation, prompting rapid growth in related research. Despite this proliferation, the literature remains fragmented across multimodal paradigms and lacks rigorous synthesis of methodological innovations, clinical safety evidence, algorithmic bias, and real-world implementation barriers—gaps that limit the field’s ability to translate algorithmic progress into clinical impact.</p> Objective <p>To systematically identify, classify, and synthesize published research on MedSAM and its derivatives, providing a comprehensive overview of methodological developments, evaluation findings, and clinical translation progress.</p> Methods <p>This scoping review followed the PRISMA-ScR guideline. We conducted a systematic search across six major databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) through June 2025.</p> Results <p>From 1,482 initial records, 45 studies met inclusion criteria. Innovation-oriented studies (<i>n</i> = 32) were classified along four methodological axes spanning multimodal paradigms: Architecture Enhancement (encoder/decoder refinements for efficiency and precision), Prompt Strategy (textual, spatial, and multimodal prompting mechanisms), Efficiency and Deployability (quantization, distillation, and system integration), and Pipeline and Application (integration into clinical workflows). Evaluation-oriented studies (<i>n</i> = 13) revealed that MedSAM outperforms general-purpose SAM by 5–20% on structured modalities (CT/MRI) but lags behind task-specific networks for delicate structures. Notably, fairness analyses from two studies (combined <i>n</i> = 1,757 subjects) report Dice disparities of up to 10% across sex, age, and BMI strata, raising concerns about potential demographic bias that warrants further investigation in larger, multi-center cohorts before firm clinical-safety conclusions can be drawn. Real-world deployment is further constrained by prompt sensitivity (5–15% Dice variation), cross-modality domain shift, regulatory compliance gaps (FDA/CE marking, HIPAA, GDPR), and absent multi-center prospective validation, all of which represent principal implementation hurdles for clinical adoption.</p> Conclusions <p>MedSAM represents a significant advancement in medical image segmentation, demonstrating a clear trajectory from large-scale architectures toward lightweight, prompt-aware, and deployable models. This synthesis provides a structured critical appraisal indicating that the evidence base remains at the proof-of-concept stage, with most studies retrospective and single-center, and emerging signals of algorithmic bias, unresolved cross-modality failure modes, and incomplete regulatory pathways represent safety and implementation considerations that should be addressed before multimodal MedSAM-based systems are more broadly deployed in clinical imaging workflows.</p>

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Medical Segment Anything Model (MedSAM): methodological innovations, evaluation practices, and clinical applications

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Wenjie Zhang,
  • Liming Luo,
  • Mengnan He,
  • Molly Beestrum,
  • Jiancheng Ye

摘要

Background

The Medical Segment Anything Model (MedSAM) has emerged as a prominent foundation model for medical image segmentation, prompting rapid growth in related research. Despite this proliferation, the literature remains fragmented across multimodal paradigms and lacks rigorous synthesis of methodological innovations, clinical safety evidence, algorithmic bias, and real-world implementation barriers—gaps that limit the field’s ability to translate algorithmic progress into clinical impact.

Objective

To systematically identify, classify, and synthesize published research on MedSAM and its derivatives, providing a comprehensive overview of methodological developments, evaluation findings, and clinical translation progress.

Methods

This scoping review followed the PRISMA-ScR guideline. We conducted a systematic search across six major databases (PubMed, Embase, Web of Science, Scopus, IEEE Xplore, ACM Digital Library) through June 2025.

Results

From 1,482 initial records, 45 studies met inclusion criteria. Innovation-oriented studies (n = 32) were classified along four methodological axes spanning multimodal paradigms: Architecture Enhancement (encoder/decoder refinements for efficiency and precision), Prompt Strategy (textual, spatial, and multimodal prompting mechanisms), Efficiency and Deployability (quantization, distillation, and system integration), and Pipeline and Application (integration into clinical workflows). Evaluation-oriented studies (n = 13) revealed that MedSAM outperforms general-purpose SAM by 5–20% on structured modalities (CT/MRI) but lags behind task-specific networks for delicate structures. Notably, fairness analyses from two studies (combined n = 1,757 subjects) report Dice disparities of up to 10% across sex, age, and BMI strata, raising concerns about potential demographic bias that warrants further investigation in larger, multi-center cohorts before firm clinical-safety conclusions can be drawn. Real-world deployment is further constrained by prompt sensitivity (5–15% Dice variation), cross-modality domain shift, regulatory compliance gaps (FDA/CE marking, HIPAA, GDPR), and absent multi-center prospective validation, all of which represent principal implementation hurdles for clinical adoption.

Conclusions

MedSAM represents a significant advancement in medical image segmentation, demonstrating a clear trajectory from large-scale architectures toward lightweight, prompt-aware, and deployable models. This synthesis provides a structured critical appraisal indicating that the evidence base remains at the proof-of-concept stage, with most studies retrospective and single-center, and emerging signals of algorithmic bias, unresolved cross-modality failure modes, and incomplete regulatory pathways represent safety and implementation considerations that should be addressed before multimodal MedSAM-based systems are more broadly deployed in clinical imaging workflows.