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Segment Anything Model for medical image segmentation: A review.

Hanguang Xiao1, Shuai Liu1, Xingyue Liu1

  • 1College of Artificial Intelligent, Chongqing University of Technology, Chongqing, 401135, China; Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots, Chongqing, 401135, China; Key Laboratory of Advanced Equipment Intelligence of Chongqing Education Commission of China, Chongqing, 401135, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|June 16, 2026
PubMed
Summary

The Segment Anything Model (SAM) shows promise for medical image segmentation but faces challenges like data gaps and precision. This review explores adaptation strategies for clinical applications.

Keywords:
Cross-modal integrationMedical image segmentationModel adaptationSegment Anything ModelTrustworthy artificial intelligence

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • The Segment Anything Model (SAM) is a foundational model for general image segmentation.
  • Medical images present unique challenges compared to natural images, limiting direct SAM application.
  • Systematic research on adapting SAM for medical imaging is scarce.

Purpose of the Study:

  • To provide a comprehensive overview of SAM adaptation in medical image segmentation.
  • To identify key challenges and developmental pathways for SAM in clinical contexts.
  • To analyze current optimization strategies and future directions for SAM in healthcare.

Main Methods:

  • Review of recent advances and optimization strategies for SAM and its variants in medical imaging.
  • In-depth analysis and comparison of existing studies on SAM for medical applications.
  • Development of an analytical framework to evaluate adaptation mechanisms.

Main Results:

  • Identified five key challenges: data gap, dimensionality, precision, semantic disconnect, and deployment hurdles.
  • Evaluated current adaptation strategies and their potential in clinical diagnosis, lesion detection, and surgical planning.
  • Summarized directions for improving model trustworthiness, interpretability, and privacy.

Conclusions:

  • SAM adaptation for medical imaging requires addressing specific clinical challenges.
  • Future directions include integrating large language models, causal representation learning, and prompt optimization.
  • This review aims to guide future SAM-based medical image segmentation research toward clinical deployment.