RRepoGEO

REPOGEO REPORT · LITE

bowang-lab/MedSAM

Default branch main · commit d71e8a1a · scanned 6/22/2026, 8:12:17 AM

GitHub: 4,316 stars · 589 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface bowang-lab/MedSAM, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    medical-imaging, segmentation, deep-learning, sam, segment-anything, computer-vision, pytorch, biomedical-imaging
  • mediumreadme#2
    Reposition the README's opening to highlight MedSAM's unique value

    Why:

    CURRENT
    # MedSAM
This is the official repository for MedSAM: Segment Anything in Medical Images.
    COPY-PASTE FIX
    # MedSAM: Segment Anything in Medical Images
This is the official repository for MedSAM, a generalist, promptable segmentation model specifically adapted and extensively pre-trained for robust medical image segmentation across diverse modalities.
  • lowreadme#3
    Add a brief comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives
MedSAM distinguishes itself from general medical imaging frameworks like MONAI or nnUNet by providing a pre-trained, generalist, and promptable segmentation model specifically fine-tuned for diverse medical images, enabling zero-shot or few-shot segmentation without extensive domain-specific training.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface bowang-lab/MedSAM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Project-MONAI/MONAI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Project-MONAI/MONAI · recommended 2×
  2. MIC-DKFZ/nnUNet · recommended 2×
  3. Slicer/Slicer · recommended 1×
  4. InsightSoftwareConsortium/ITK · recommended 1×
  5. opencv/opencv · recommended 1×
  • CATEGORY QUERY
    How can I automatically segment anatomical structures in various medical imaging modalities?
    you: not recommended
    AI recommended (in order):
    1. MONAI (Project-MONAI/MONAI)
    2. nnUNet (MIC-DKFZ/nnUNet)
    3. 3D Slicer (Slicer/Slicer)
    4. ITK (InsightSoftwareConsortium/ITK)
    5. OpenCV (opencv/opencv)
    6. Google Cloud Healthcare API
    7. MATLAB Image Processing Toolbox
    8. MATLAB Deep Learning Toolbox

    AI recommended 8 alternatives but never named bowang-lab/MedSAM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a fast deep learning tool to accurately segment 3D medical scans.
    you: not recommended
    AI recommended (in order):
    1. MONAI (Project-MONAI/MONAI)
    2. nnUNet (MIC-DKFZ/nnUNet)
    3. PyTorch (pytorch/pytorch)
    4. TorchIO (fepegar/torchio)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. NVIDIA Clara Train SDK

    AI recommended 7 alternatives but never named bowang-lab/MedSAM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of bowang-lab/MedSAM?
    pass
    AI named bowang-lab/MedSAM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts bowang-lab/MedSAM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bowang-lab/MedSAM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo bowang-lab/MedSAM solve, and who is the primary audience?
    pass
    AI named bowang-lab/MedSAM explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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bowang-lab/MedSAM — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite