# MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts

arXiv: https://arxiv.org/abs/2305.10799 · HTML: https://mlsanity.com/paper/2305.10799/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 18 May 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)
- Synthetic data and self-improvement for LLMs / [Medical synthetic data and agents](https://mlsanity.com/synth/medical-synthetic-data-and-agents/)

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts](https://mlsanity.com/paper/2111.02358/) (2021)
- [Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm](https://mlsanity.com/paper/2110.05208/) (2021)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning](https://mlsanity.com/paper/2102.10407/) (2021)
- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models](https://mlsanity.com/paper/2303.05977/) (2023)

## Built on by (most cited first, 30 of 51)

- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision](https://mlsanity.com/paper/2310.18689/) (2023)
- [Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare](https://mlsanity.com/paper/2310.17956/) (2023)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [Vision-Language Models for Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/2502.07855/) (2025)
- [CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches](https://mlsanity.com/paper/2409.17457/) (2024)
- [Towards a Personal Health Large Language Model](https://mlsanity.com/paper/2406.06474/) (2024)
- [Medical Vision Language Pretraining: A survey](https://mlsanity.com/paper/2312.06224/) (2023)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA](https://mlsanity.com/paper/2405.20421/) (2024)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model](https://mlsanity.com/paper/2411.12783/) (2024)
- [T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency](https://mlsanity.com/paper/2312.01529/) (2023)
- [Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs](https://mlsanity.com/paper/2505.00744/) (2025)
- [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025)
- [Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning](https://mlsanity.com/paper/2509.19090/) (2025)
- [Do Medical Vision Language Models Actually See? A Counterfactual Grounding Framework and Hard-Negative Contrastive Training for Visually-Reliant Medical VLMs](https://mlsanity.com/paper/2607.03647/) (2026)
- [VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis](https://mlsanity.com/paper/2410.08397/) (2024)
- [Med3DInsight: Enhancing 3D Medical Image Understanding with 2D Multi-Modal Large Language Models](https://mlsanity.com/paper/2403.05141/) (2024)
- [Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering](https://mlsanity.com/paper/2404.16192/) (2024)
- [SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging](https://mlsanity.com/paper/2504.10642/) (2025)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2507.02200/) (2025)
- [Adaptation of Foundation Models for Medical Image Analysis: Strategies, Challenges, and Future Directions](https://mlsanity.com/paper/2511.01284/) (2025)
- [ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue](https://mlsanity.com/paper/2409.17610/) (2024)
- [Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA](https://mlsanity.com/paper/2503.02034/) (2025)
- [Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data](https://mlsanity.com/paper/2504.09967/) (2025)
- [Recent Advances in Medical Image Classification](https://mlsanity.com/paper/2506.04129/) (2025)

## Abstract (arXiv)

Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications. In this paper, we consider developing a VLP model in the medical domain for making computer-aided diagnoses (CAD) based on image scans and text descriptions in electronic health records, as done in practice. To achieve our goal, we present a lightweight CAD system MedBLIP, a new paradigm for bootstrapping VLP from off-the-shelf frozen pre-trained image encoders and frozen large language models. We design a MedQFormer module to bridge the gap between 3D medical images and 2D pre-trained image encoders and language models as well. To evaluate the effectiveness of our MedBLIP, we collect more than 30,000 image volumes from five public Alzheimer's disease (AD) datasets, i.e., ADNI, NACC, OASIS, AIBL, and MIRIAD. On this largest AD dataset we know, our model achieves the SOTA performance on the zero-shot classification of healthy, mild cognitive impairment (MCI), and AD subjects, and shows its capability of making medical visual question answering (VQA). The code and pre-trained models is available online: https://github.com/Qybc/MedBLIP.