RRepoGEO

REPOGEO REPORT · LITE

chaoyi-wu/RadFM

Default branch main · commit 8d798c55 · scanned 6/12/2026, 3:52:33 AM

GitHub: 553 stars · 66 forks

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 chaoyi-wu/RadFM, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clearly state RadFM's purpose

    Why:

    CURRENT
    # RadFM
The official code for the paper "Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data"
    COPY-PASTE FIX
    # RadFM: A Generalist Foundation Model for Radiology

This repository provides the official code for RadFM, a pioneering generalist foundation model designed for radiology. RadFM leverages web-scale 2D and 3D medical data to enable multi-modal generative capabilities, supporting both 2D and 3D scans, multi-image input, and visual-language interleaving cases. It is the official implementation for the paper "Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data".
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Insert project website URL here, e.g., the 'Website' link from your README or a dedicated project page]

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 chaoyi-wu/RadFM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MONAI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MONAI · recommended 2×
  2. PyTorch Lightning · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. NVIDIA Clara Train SDK · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to build a generalist AI model for radiology using both 2D and 3D medical scans?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. PyTorch Lightning
    3. Hugging Face Transformers
    4. NVIDIA Clara Train SDK
    5. TensorFlow
    6. OpenCV
    7. DVC

    AI recommended 7 alternatives but never named chaoyi-wu/RadFM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a foundation model for medical imaging that supports multi-modal 2D/3D and text inputs.
    you: not recommended
    AI recommended (in order):
    1. Med-PaLM 2
    2. BioMed-CLIP
    3. MONAI
    4. Rad-BERT / Clinical-BERT
    5. OpenCLIP

    AI recommended 5 alternatives but never named chaoyi-wu/RadFM. 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 chaoyi-wu/RadFM?
    pass
    AI named chaoyi-wu/RadFM explicitly

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

  • If a team adopts chaoyi-wu/RadFM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chaoyi-wu/RadFM 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 chaoyi-wu/RadFM solve, and who is the primary audience?
    pass
    AI named chaoyi-wu/RadFM explicitly

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

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chaoyi-wu/RadFM — 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