# Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals

arXiv: https://arxiv.org/abs/2510.06280 · HTML: https://mlsanity.com/paper/2510.06280/

## Key facts

- Cited by 1 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 23 of its 32 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Placed in

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

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [FairCLIP: Harnessing Fairness in Vision-Language Learning](https://mlsanity.com/paper/2403.19949/) (2024)
- [MEDFAIR: Benchmarking Fairness for Medical Imaging](https://mlsanity.com/paper/2210.01725/) (2022)
- [Vision Language Models are Biased](https://mlsanity.com/paper/2505.23941/) (2025)
- [SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples](https://mlsanity.com/paper/2312.00825/) (2023)
- [Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence](https://mlsanity.com/paper/2006.08242/) (2020)
- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Identifying Implicit Social Biases in Vision-Language Models](https://mlsanity.com/paper/2411.00997/) (2024)
- [MultiModal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision Language Models](https://mlsanity.com/paper/2303.12734/) (2023)
- [Joint Vision-Language Social Bias Removal for CLIP](https://mlsanity.com/paper/2411.12785/) (2024)
- [BiasDora: Exploring Hidden Biased Associations in Vision-Language Models](https://mlsanity.com/paper/2407.02066/) (2024)
- [A Unified Framework and Dataset for Assessing Societal Bias in Vision-Language Models](https://mlsanity.com/paper/2402.13636/) (2024)
- [Gender Bias in Text-to-Video Generation Models: A case study of Sora](https://mlsanity.com/paper/2501.01987/) (2024)
- [The Bias of Harmful Label Associations in Vision-Language Models](https://mlsanity.com/paper/2402.07329/) (2024)
- [Vision-Language Models display a strong gender bias](https://mlsanity.com/paper/2508.11262/) (2025)
- [Freeze and Reveal: Exposing Modality Bias in Vision-Language Models](https://mlsanity.com/paper/2508.07432/) (2025)
- [Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models](https://mlsanity.com/paper/2501.12433/) (2025)
- [Visual Cues of Gender and Race are Associated with Stereotyping in Vision-Language Models](https://mlsanity.com/paper/2503.05093/) (2025)

## Built on by (most cited first, 1 of 1)

- [IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models](https://mlsanity.com/paper/2602.12659/) (2026)

## Abstract (arXiv)

Vision language models (VLMs), such as CLIP and OpenCLIP, can encode and reflect stereotypical associations between medical professions and demographic attributes learned from web-scale data. We present an evaluation protocol for healthcare settings that quantifies associated biases and assesses their operational risk. Our methodology (i) defines a taxonomy spanning clinicians and allied healthcare roles (e.g., surgeon, cardiologist, dentist, nurse, pharmacist, technician), (ii) curates a profession-aware prompt suite to probe model behavior, and (iii) benchmarks demographic skew against a balanced face corpus. Empirically, we observe consistent demographic biases across multiple roles and vision models. Our work highlights the importance of bias identification in critical domains such as healthcare as AI-enabled hiring and workforce analytics can have downstream implications for equity, compliance, and patient trust.