REPOGEO REPORT · LITE

shibing624/MedicalGPT

Default branch main · commit ccc05f4b · scanned 6/29/2026, 3:13:13 AM

GitHub: 5,551 stars · 766 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

28 /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

2 / 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 shibing624/MedicalGPT, 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 README H1 and introduction to emphasize comprehensive medical LLM pipeline

Why:

CURRENT

# MedicalGPT: Training Medical GPT Model
MedicalGPT training medical GPT model with ChatGPT training pipeline, implemantation of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning), DPO(Direct Preference Optimization) and standalone OPD(On-Policy Distillation).

COPY-PASTE FIX

# MedicalGPT: A Comprehensive Pipeline for Training Medical Large Language Models
**MedicalGPT** provides a comprehensive, end-to-end pipeline for training specialized medical Large Language Models (LLMs), implementing advanced techniques including Pretraining (PT), Supervised Finetuning (SFT), RLHF (Reward Modeling and Reinforcement Learning), DPO (Direct Preference Optimization), ORPO, GRPO, and On-Policy Distillation (OPD).

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/shibing624/MedicalGPT
```

lowtopics#3

Expand repository topics to include LLM training pipeline specifics

Why:

CURRENT

chatgpt, dpo, gpt, llama, llm, medical, medicalgpt

COPY-PASTE FIX

chatgpt, dpo, gpt, llama, llm, medical, medicalgpt, llm-finetuning, llm-training-pipeline, healthcare-ai, rlhf

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 shibing624/MedicalGPT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
OpenAI API · recommended 2×
Hugging Face Datasets · recommended 1×
PyTorch · recommended 1×
TensorFlow · recommended 1×

CATEGORY QUERY
How can I train a custom large language model specifically for medical applications?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Hugging Face Datasets
3. PyTorch
4. TensorFlow
5. NVIDIA NeMo Framework
6. OpenAI API
7. Google Cloud Vertex AI
8. Amazon SageMaker
9. Microsoft Azure Machine Learning
AI recommended 9 alternatives but never named shibing624/MedicalGPT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools are available for fine-tuning LLMs with DPO or RLHF for healthcare data?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. TRL
3. DeepSpeed-Chat
4. OpenAI API
5. GPT-4
6. GPT-3.5
7. Axolotl
8. RL4LMs
9. Lit-GPT
AI recommended 9 alternatives but never named shibing624/MedicalGPT. 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 shibing624/MedicalGPT?
pass
AI did not name shibing624/MedicalGPT — 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 shibing624/MedicalGPT in production, what risks or prerequisites should they evaluate first?
pass
AI named shibing624/MedicalGPT 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 shibing624/MedicalGPT solve, and who is the primary audience?
pass
AI named shibing624/MedicalGPT 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 shibing624/MedicalGPT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/shibing624/MedicalGPT.svg)](https://repogeo.com/en/r/shibing624/MedicalGPT)

HTML

<a href="https://repogeo.com/en/r/shibing624/MedicalGPT"><img src="https://repogeo.com/badge/shibing624/MedicalGPT.svg" alt="RepoGEO" /></a>

Pro

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shibing624/MedicalGPT — 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