RRepoGEO

REPOGEO REPORT · LITE

maziyarpanahi/openmed

Default branch master · commit eefe599e · scanned 6/29/2026, 1:41:35 AM

GitHub: 3,879 stars · 435 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
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 maziyarpanahi/openmed, 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 H2 to specify clinical text NLP

    Why:

    CURRENT
    <h2>Local-first healthcare AI that never leaves the device</h2>
    COPY-PASTE FIX
    <h2>Local-first healthcare NLP for clinical text: NER & HIPAA PII de-identification on-device</h2>
  • mediumreadme#2
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'Why OpenMed? Local-first, HIPAA-compliant, On-device NLP', explicitly contrasting its unique value proposition against common alternatives.
  • lowtopics#3
    Refine topics for stronger signal on specific tasks

    Why:

    CURRENT
    clinical-nlp, healthcare, hipaa, ios, llm, local-llm, mlx, ner, nlp, on-device, on-premise, pii, pii-detection, python, sovereign-ai, swift, swiftui
    COPY-PASTE FIX
    Add 'medical-nlp' and 'pii-de-identification' to the existing topics.

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 maziyarpanahi/openmed
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
spaCy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. spaCy · recommended 2×
  2. Stanza · recommended 2×
  3. John Snow Labs NLP for Healthcare · recommended 1×
  4. Microsoft Presidio · recommended 1×
  5. NIST De-identification Tool (NDAT) · recommended 1×
  • CATEGORY QUERY
    How can I perform HIPAA-compliant PII de-identification on clinical text locally?
    you: not recommended
    AI recommended (in order):
    1. John Snow Labs NLP for Healthcare
    2. Microsoft Presidio
    3. spaCy
    4. Stanza
    5. NIST De-identification Tool (NDAT)

    AI recommended 5 alternatives but never named maziyarpanahi/openmed. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best on-device NLP libraries for medical entity extraction in Python?
    you: not recommended
    AI recommended (in order):
    1. spaCy
    2. scispacy
    3. John Snow Labs Spark NLP (Healthcare)
    4. Hugging Face Transformers
    5. Stanza
    6. NLTK (Natural Language Toolkit)

    AI recommended 6 alternatives but never named maziyarpanahi/openmed. 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 maziyarpanahi/openmed?
    pass
    AI named maziyarpanahi/openmed explicitly

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

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

    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 maziyarpanahi/openmed. 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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MARKDOWN (README)
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<a href="https://repogeo.com/en/r/maziyarpanahi/openmed"><img src="https://repogeo.com/badge/maziyarpanahi/openmed.svg" alt="RepoGEO" /></a>
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maziyarpanahi/openmed — 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