Med-Flamingo
Few-Shot Medical Visual Question Answering
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Med-Flamingo is a multimodal few-shot learner for medicine from Stanford researchers. Built on OpenFlamingo-9B and pretrained on interleaved image-text data from medical textbooks and PubMed Central, it can answer questions about a new medical image after seeing just a handful of examples.
Try Med-Flamingo-Style Visual Question Answering
Upload a medical image and ask a question about it â the demo shows the interleaved image-and-text reasoning Med-Flamingo introduced for medicine.
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What is Med-Flamingo?
Med-Flamingo is a medical vision-language model released in 2023 by Michael Moor and colleagues at Stanford together with collaborators (Moor et al., "Med-Flamingo: a Multimodal Medical Few-shot Learner"). It adapts OpenFlamingo-9B â an open reimplementation of DeepMind's Flamingo â to the medical domain.
The model was continued-pretrained on interleaved image-text sequences from medical textbooks (the MTB dataset) and PMC-OA figure-caption pairs. Because it consumes images and text interleaved in one context, it supports in-context ("few-shot") learning: show it a few image-question-answer examples and it generalises to a new image without fine-tuning.
On VQA-RAD, PathVQA and a new Visual USMLE benchmark, clinicians rated Med-Flamingo's generative answers higher than baselines, but the authors are explicit that it is an early research prototype that can hallucinate and is not fit for clinical use.
Latest Development
Released July 2023 by Stanford researchers and collaborators (Moor et al., 2023)
Open weights on GitHub / Hugging Face for research; based on OpenFlamingo-9B
Key Features
In-context learning for medical images
Core Capabilities
Few-shot medical visual question answering
Interleaved image + text inputs in one prompt
Generative (free-text) answers, not just multiple choice
Pretrained on medical textbooks and PubMed Central figures
Built on OpenFlamingo-9B (LLaMA-7B language backbone)
Evaluated on VQA-RAD, PathVQA and Visual USMLE
Clinician-rated evaluation protocol
Open research weights
Performance Benchmarks
Evaluation described in the Med-Flamingo paper; clinicians scored generative answers rather than a single accuracy number
VQA-RAD
Few-shotRadiology visual question answering with in-context examples
PathVQA
Few-shotPathology image question answering
Visual USMLE
New benchmarkUSMLE-style questions that require looking at an image, introduced with the paper
Clinician rating
+ vs baselinesHuman experts preferred Med-Flamingo answers over the compared baselines on average
Use Cases
Multimodal Research
Study in-context learning for medical images with an open model.
Image-Grounded Q&A
Answer free-text questions about a medical image for clinician review.
Medical Education
Image-based USMLE-style practice with explanations.
How to Use Med-Flamingo
Research code and weights from the authors
Get the Model
Code and weights are published in the authors' GitHub repository and on Hugging Face; you also need the OpenFlamingo and LLaMA-7B base weights.
Integration Options
Compose a prompt of a few image-question-answer examples followed by the new image and question.
- âĸ Few-shot VQA on radiology images
- âĸ Few-shot VQA on pathology images
- âĸ Visual USMLE-style question answering
- âĸ Baseline for multimodal medical research
Deployment Considerations
Plan for privacy, validation and human oversight before putting Med-Flamingo anywhere near patient data.
- âĸ De-identify data and comply with HIPAA / GDPR
- âĸ Validate on your own institution's data before use
- âĸ Keep a licensed clinician in the loop for every output
- âĸ Document limitations and intended use for auditors
Important Considerations
Clinical Validation Required
Med-Flamingo is a research model. Its outputs must be reviewed by qualified healthcare professionals before any clinical use.
Regulatory Compliance
The model is not a cleared or approved medical device. Ensure compliance with local healthcare regulations and data-protection rules (e.g. HIPAA, GDPR) before deployment.
Have a photo of a visible symptom?
The models described on this page are research tools for clinical imaging. If what you have is an everyday photo â a rash, a mole, a nail, a wound â AI Doctor Scan reads it and returns a free urgency level and a plain-language summary in about 30 seconds. It does not name a diagnosis, and it needs no account.
Try AI Doctor Scan â freeMed-Flamingo vs Other Medical Multimodal Models
What few-shot learning changes
Med-Flamingo
Stanford few-shot medical VQA
- âAdapts to new image tasks from a few examples
- âInterleaved image-text prompting
- âFree-text generative answers
- âOpen research weights
- âIntroduced the Visual USMLE benchmark
- âClinician-rated evaluation
Research on in-context learning and generative medical VQA
Other Medical AI Models
How it compares
- ÃGeneral LLMs (GPT-4, Gemini) are stronger at open-ended reasoning but are closed and costly
- ÃLarger open models (Meditron-70B, MedGemma-27B) need far more GPU memory
- ÃText-only models cannot look at images
- ÃImage-only classifiers cannot explain their findings in language
- ÃCommercial products are validated but not inspectable
- ÃMost alternatives publish fewer reproducible benchmarks
Pick by modality (text vs. image), licence, and the hardware you actually have
Frequently Asked Questions
Common questions about Med-Flamingo
Who developed Med-Flamingo?
Michael Moor and colleagues at Stanford University with collaborators (2023). It is not a Google model.
What does "few-shot" mean here?
You put a few example image-question-answer triples in the prompt; the model then answers about a new image without any fine-tuning.
What is Med-Flamingo built on?
OpenFlamingo-9B, an open reimplementation of DeepMind's Flamingo architecture with a LLaMA-7B language backbone.
Is Med-Flamingo safe to use clinically?
No. The authors describe it as an early research prototype that can hallucinate; it must not be used for patient care.
Does this demo run Med-Flamingo itself?
The demo uses Dr7.ai's multimodal medical assistant to show image question answering. For the exact model, run the authors' code.