RRepoGEO

REPOGEO REPORT · LITE

AQ-MedAI/MedResearcher-R1

Default branch main · commit e85c4927 · scanned 6/7/2026, 3:57:55 AM

GitHub: 508 stars · 45 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 AQ-MedAI/MedResearcher-R1, 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
  • highreadme#1
    Reposition the README H1 to specify its nature as a framework for data generation and reasoning

    Why:

    CURRENT
    # MedResearcher-R1: Knowledge-Informed Trajectory Synthesis Approach
    COPY-PASTE FIX
    # MedResearcher-R1: A Framework for Medical AI Training Data Generation & Knowledge-Informed Reasoning
  • hightopics#2
    Add specific topics related to training data generation and knowledge graph construction for AI

    Why:

    CURRENT
    agent, deepresearch, llm, medical-ai
    COPY-PASTE FIX
    agent, deepresearch, llm, medical-ai, training-data-generation, knowledge-graph, ai-reasoning, medical-nlp, framework
  • mediumreadme#3
    Add an explicit problem statement and target audience section to the README

    Why:

    COPY-PASTE FIX
    ## What Problem Does MedResearcher-R1 Solve?
Developing robust domain-specific AI, especially in complex fields like medicine, requires high-quality, contextually rich training data and verifiable reasoning paths. MedResearcher-R1 provides an end-to-end framework for researchers and developers to automatically generate this critical data, construct intelligent knowledge graphs, and synthesize multi-turn reasoning trajectories, significantly accelerating the development of advanced medical AI agents.

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 AQ-MedAI/MedResearcher-R1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GCP Healthcare API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GCP Healthcare API · recommended 1×
  2. Amazon Comprehend Medical · recommended 1×
  3. Microsoft Azure Health Bot Service · recommended 1×
  4. Labelbox · recommended 1×
  5. Prodigy · recommended 1×
  • CATEGORY QUERY
    How to generate high-quality training data for medical AI reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. GCP Healthcare API
    2. Amazon Comprehend Medical
    3. Microsoft Azure Health Bot Service
    4. Labelbox
    5. Prodigy
    6. Snorkel
    7. Gleamer AI

    AI recommended 7 alternatives but never named AQ-MedAI/MedResearcher-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework to build knowledge graphs and generate reasoning paths for domain-specific AI?
    you: not recommended
    AI recommended (in order):
    1. TypeDB
    2. Stardog
    3. Ontotext GraphDB
    4. Neo4j
    5. RDFox
    6. Apache Jena
    7. Pytorch Geometric
    8. Deep Graph Library

    AI recommended 8 alternatives but never named AQ-MedAI/MedResearcher-R1. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 AQ-MedAI/MedResearcher-R1?
    pass
    AI named AQ-MedAI/MedResearcher-R1 explicitly

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

  • If a team adopts AQ-MedAI/MedResearcher-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AQ-MedAI/MedResearcher-R1 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 AQ-MedAI/MedResearcher-R1 solve, and who is the primary audience?
    pass
    AI named AQ-MedAI/MedResearcher-R1 explicitly

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

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AQ-MedAI/MedResearcher-R1 — 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