# How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?

arXiv: https://arxiv.org/abs/2409.00543 · HTML: https://mlsanity.com/paper/2409.00543/

## Key facts

- Cited by 2 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 29 of its 32 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [SLIP: Self-supervision meets Language-Image Pre-training](https://mlsanity.com/paper/2112.12750/) (2021)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias](https://mlsanity.com/paper/2305.19894/) (2023)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Improving Zero-shot Generalization and Robustness of Multi-modal Models](https://mlsanity.com/paper/2212.01758/) (2022)
- [M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization](https://mlsanity.com/paper/2307.08347/) (2023)
- [Learning to Prompt with Text Only Supervision for Vision-Language Models](https://mlsanity.com/paper/2401.02418/) (2024)
- [Stare at What You See: Masked Image Modeling without Reconstruction](https://mlsanity.com/paper/2211.08887/) (2022)
- [Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework](https://mlsanity.com/paper/2403.07636/) (2024)
- [Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis](https://mlsanity.com/paper/2303.13391/) (2023)
- [IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training](https://mlsanity.com/paper/2310.07355/) (2023)
- [Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images](https://mlsanity.com/paper/2310.07027/) (2023)
- [T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency](https://mlsanity.com/paper/2312.01529/) (2023)
- [Exploring the Zero-Shot Capabilities of Vision-Language Models for Improving Gaze Following](https://mlsanity.com/paper/2406.03907/) (2024)
- [G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training](https://mlsanity.com/paper/2312.01522/) (2023)
- [Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training](https://mlsanity.com/paper/2401.01179/) (2024)

## Built on by (most cited first, 2 of 2)

- [LLaDA-MedV: Exploring Large Language Diffusion Models for Biomedical Image Understanding](https://mlsanity.com/paper/2508.01617/) (2025)
- [Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening](https://mlsanity.com/paper/2609.21763/) (2026)

## Abstract (arXiv)

Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-scale medical image-text pair pre-training. However, the performance of these tasks can be heavily influenced by the variability in textual prompts describing the categories, necessitating robustness in MedVLP models to diverse prompt styles. Yet, this sensitivity remains underexplored. In this work, we are the first to systematically assess the sensitivity of three widely-used MedVLP methods to a variety of prompts across 15 different diseases. To achieve this, we designed six unique prompt styles to mirror real clinical scenarios, which were subsequently ranked by interpretability. Our findings indicate that all MedVLP models evaluated show unstable performance across different prompt styles, suggesting a lack of robustness. Additionally, the models' performance varied with increasing prompt interpretability, revealing difficulties in comprehending complex medical concepts. This study underscores the need for further development in MedVLP methodologies to enhance their robustness to diverse zero-shot prompts.