RRepoGEO

REPOGEO REPORT · LITE

Jianing-Qiu/Awesome-Healthcare-Foundation-Models

Default branch main · commit 81a0d56b · scanned 6/4/2026, 11:57:47 AM

GitHub: 515 stars · 52 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 Jianing-Qiu/Awesome-Healthcare-Foundation-Models, 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
  • highabout#1
    Add a concise description to the 'About' section

    Why:

    COPY-PASTE FIX
    A curated list of awesome large AI models (LAMs) and foundation models specifically for healthcare applications, including LLMs, LVMs, and LMMs.
  • mediumreadme#2
    Strengthen the README's opening sentence to emphasize its 'list' nature

    Why:

    CURRENT
    Curated list of awesome large AI models (LAMs), or foundation models, in healthcare. We organize the current LAMs into four categories: large language models (LLMs), large vision models (LVMs), large audio models, and large multi-modal models (LMMs).
    COPY-PASTE FIX
    This repository provides a comprehensive, curated list of awesome large AI models (LAMs) and foundation models specifically tailored for healthcare applications. We organize these resources into key categories: large language models (LLMs), large vision models (LVMs), large audio models, and large multi-modal models (LMMs).

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 Jianing-Qiu/Awesome-Healthcare-Foundation-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AlphaFold
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AlphaFold · recommended 1×
  2. Isomorphic Labs · recommended 1×
  3. PathAI · recommended 1×
  4. Paige · recommended 1×
  5. Paige ProstateDetect · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of large AI models for medical diagnosis?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold
    2. Isomorphic Labs
    3. PathAI
    4. Paige
    5. Paige ProstateDetect
    6. IBM Watson Health
    7. Watson for Oncology
    8. Aidoc
    9. Viz.ai
    10. Microsoft Healthcare Bot
    11. Azure AI for Health

    AI recommended 11 alternatives but never named Jianing-Qiu/Awesome-Healthcare-Foundation-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the leading multimodal AI models available for medical imaging research?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. Med-PaLM 2
    3. Swin UNETR
    4. UNet
    5. Vision Transformers (ViT)
    6. TransUNet
    7. CLIP
    8. MedCLIP

    AI recommended 8 alternatives but never named Jianing-Qiu/Awesome-Healthcare-Foundation-Models. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Jianing-Qiu/Awesome-Healthcare-Foundation-Models?
    pass
    AI did not name Jianing-Qiu/Awesome-Healthcare-Foundation-Models — likely talking about a different project

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

  • If a team adopts Jianing-Qiu/Awesome-Healthcare-Foundation-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Jianing-Qiu/Awesome-Healthcare-Foundation-Models 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 Jianing-Qiu/Awesome-Healthcare-Foundation-Models solve, and who is the primary audience?
    pass
    AI did not name Jianing-Qiu/Awesome-Healthcare-Foundation-Models — likely talking about a different project

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

Embed your GEO score

Drop this badge into the README of Jianing-Qiu/Awesome-Healthcare-Foundation-Models. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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Jianing-Qiu/Awesome-Healthcare-Foundation-Models — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite