REPOGEO REPORT · LITE
richard-peng-xia/awesome-multimodal-in-medical-imaging
Default branch main · commit 228ab07b · scanned 6/14/2026, 2:27:50 PM
GitHub: 965 stars · 81 forks
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 richard-peng-xia/awesome-multimodal-in-medical-imaging, 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.
- highreadme#1Reposition README opening to emphasize its nature as a curated list
Why:
CURRENTThis repository includes resources on several applications of multi-modal learning in medical imaging, including papers related to large language models (LLM).
COPY-PASTE FIXThis repository is a curated collection of academic papers and resources on applications of multi-modal learning in medical imaging, with a special focus on large language models (LLM). It serves as a central hub for researchers, students, and practitioners to discover and organize essential resources in this rapidly evolving field.
- mediumhomepage#2Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXhttps://github.com/richard-peng-xia/awesome-multimodal-in-medical-imaging
- lowtopics#3Add 'awesome-list' and 'curated-list' topics
Why:
CURRENTlarge-language-models, large-multimodal-models, medical-imaging, medical-report-generation, multimodal-deep-learning, multimodal-large-language-models, multimodal-learning, visual-question-answering
COPY-PASTE FIXlarge-language-models, large-multimodal-models, medical-imaging, medical-report-generation, multimodal-deep-learning, multimodal-large-language-models, multimodal-learning, visual-question-answering, awesome-list, curated-list, resource-collection
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.
- PubMed · recommended 1×
- PubMed Central · recommended 1×
- IEEE Xplore Digital Library · recommended 1×
- IEEE Transactions on Medical Imaging · recommended 1×
- IEEE Journal of Biomedical and Health Informatics · recommended 1×
- CATEGORY QUERYWhere can I find academic papers on multimodal deep learning for medical image analysis?you: not recommendedAI recommended (in order):
- PubMed
- PubMed Central
- IEEE Xplore Digital Library
- IEEE Transactions on Medical Imaging
- IEEE Journal of Biomedical and Health Informatics
- arXiv
- Google Scholar
- Scopus
- Web of Science
- MICCAI
- IPMI
- ISBI
- MIDL
- NeurIPS
- ICML
- CVPR
- ECCV
- AAAI
AI recommended 18 alternatives but never named richard-peng-xia/awesome-multimodal-in-medical-imaging. This is the gap to close.
Show full AI answer
- CATEGORY QUERYLooking for research on integrating large language models with medical imaging for diagnosis.you: not recommendedAI recommended (in order):
- RadBERT
- CheXpert
- RadFM
- Med-PaLM M
- Flamingo
- LLaVA-Med
- VQA-Med
- SLAKE
- IBM Watson Health Imaging
- Nuance PowerScribe One with AI
- PathLLM
- HistoGPT
AI recommended 12 alternatives but never named richard-peng-xia/awesome-multimodal-in-medical-imaging. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 richard-peng-xia/awesome-multimodal-in-medical-imaging?passAI did not name richard-peng-xia/awesome-multimodal-in-medical-imaging — 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 richard-peng-xia/awesome-multimodal-in-medical-imaging in production, what risks or prerequisites should they evaluate first?passAI did not name richard-peng-xia/awesome-multimodal-in-medical-imaging — 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?
- In one sentence, what problem does the repo richard-peng-xia/awesome-multimodal-in-medical-imaging solve, and who is the primary audience?passAI did not name richard-peng-xia/awesome-multimodal-in-medical-imaging — 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 richard-peng-xia/awesome-multimodal-in-medical-imaging. 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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richard-peng-xia/awesome-multimodal-in-medical-imaging — 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