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Using these measures, we quantify gender bias as a form of statistical bias along three dimensions. First, we examine the extent to which social categories are associated with a specific gender in images and texts. Second, we examine the extent to which women are represented, compared with men, across all social categories in images and texts. Third, we compare the gender associations in our image and text data with the empirical representation of women and men in public opinion and US census data on occupations. This allows us to test not only whether gender bias is statistically stronger in images than texts, but also whether this bias reflects a distorted representation of the empirical distribution of women and men in society.

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Although these analyses support our prediction that online gender bias is more prevalent in images than texts, an open question is whether online images present a biased representation of the empirical distribution of gender in society. Next, we show that online images exhibit significantly stronger gender bias than public opinion and 2019 US census data on occupations.

The rise of images in popular internet culture may come at a critical social cost. We have found that gender bias online is more prevalent and more psychologically potent in images than text. The growing centrality of visual content in our daily information diets may exacerbate gender bias by magnifying its digital presence and deepening its psychological entrenchment. This problem is expected to affect the well-being of, social status of and economic opportunities for not only women, who are systematically underrepresented in online images, but also men in female-typed categories such as care-oriented occupations41,42.

Our findings are especially alarming given that image-based social media platforms such as Instagram, Snapchat and TikTok are surging in popularity, accelerating the mass production and circulation of images. In parallel, popular search engines such as Google are increasingly incorporating images into their core functionality, for example, by including images as a default part of text-based searches43. Perhaps the apex of these developments is the widespread adoption of text-to-image artificial intelligence (AI) models that allow users to automatically generate images by means of textual prompts, further accelerating the production and circulation of images. Current work identifies salient gender and racial biases in the images that these AI models generate44, signalling that they may also intensify the large-scale spread of social biases. Consistent with related studies45, our work suggests that gender biases in multimodal AI may stem in part from the fact that they are trained on public images from platforms such as Google and Wikipedia, which are rife with gender bias according to our measures.

To keep pace with the evolving landscape of bias online, it is important for computational social scientists to expand beyond the analysis of textual data to include other content modalities that offer distinct ways of transmitting cultural information. Indeed, decades of research maintain that images lie at the foundation of human cognition11,12,25,48 and may have provided the first means of human communication and culture24,49. It is therefore difficult to imagine how the science of human culture can be complete without a multimodal framework. Exploring the implications of an image-centric social reality for the evolution of human cognition and culture is a ripe direction for future research. Our study identifies one of many implications of this cultural shift concerning the amplification of social bias, stemming from the salient way in which images present demographic information when depicting social categories. Addressing the societal impact of this ascending visual culture will be essential in building a fair and inclusive future for the internet, and developing a multimodal approach to computational social science is a crucial step in this direction.

Here we outline the computational and experimental techniques we use to compare gender bias in online images and texts. We begin by describing the methods of data collection and analyses developed for the observational component of our study. Then we detail the study design deployed in our online search experiment. The preregistration for our online experiment is available at Note that this study is a successful replication of a previous study with a nearly identical design, except the original study did not include a control condition nor several versions of the text condition; the preregistration of the previous study is available at

Medical artificial intelligence (AI) has achieved significant progress in recent years with the notable evolution of deep learning techniques1,3,4. For instance, deep neural networks have matched or surpassed the accuracy of clinical experts in various applications5, such as referral recommendations for sight-threatening retinal diseases6 and pathology detection in chest X-ray images7. These models are typically developed using large volumes of high-quality labels, which requires expert assessment and laborious workload1,2. However, the scarcity of experts with domain knowledge cannot meet such an exhaustive requirement, leaving vast amounts of medical data unlabelled and unexploited.

Figure 1 gives an overview of the construction and application of RETFound. For construction of RETFound, we curated 904,170 CFP in which 90.2% of images came from MEH-MIDAS and 9.8% from Kaggle EyePACS33, and 736,442 OCT in which 85.2% of them came from MEH-MIDAS and 14.8% from ref. 34. MEH-MIDAS is a retrospective dataset that includes the complete ocular imaging records of 37,401 patients with diabetes who were seen at Moorfields Eye Hospital between January 2000 and March 2022. After self-supervised pretraining on these retinal images, we evaluated the performance and generalizability of RETFound in adapting to diverse ocular and oculomic tasks. We selected publicly available datasets for the tasks of ocular disease diagnosis. Details are listed in Supplementary Table 1. For the tasks of ocular disease prognosis and systemic disease prediction, we used a cohort from the Moorfields AlzEye study (MEH-AlzEye) that links ophthalmic data of 353,157 patients, who attended Moorfields Eye Hospital between 2008 and 2018, with systemic disease data from hospital admissions across the whole of England35. We also used UK Biobank36 for external evaluation in predicting systemic diseases. The validation datasets used for ocular disease diagnosis are sourced from several countries, whereas systemic disease prediction was solely validated on UK datasets due to limited availability of this type of longitudinal data. Our assessment of generalizability for systemic disease prediction was therefore based on many tasks and datasets, but did not extend to vastly different geographical settings. Details of the clinical datasets are listed in Supplementary Table 2 (data selection is introduced in the Methods section).

To gain insights into the inner-workings of RETFound leading to its superior performance and label efficiency in downstream tasks, we performed qualitative analyses of the pretext task used for self-supervised pretraining and task-specific decisions of RETFound (Extended Data Fig. 6). The pretext task of RETFound allows models to learn retina-specific context, including anatomical structures and disease lesions. As shown in Extended Data Fig. 6a, RETFound was able to reconstruct major anatomical structures, including the optic nerve and large vessels on CFP, and the nerve fibre layer and retinal pigment epithelium on OCT, despite 75% of the retinal image being masked. This demonstrates that RETFound has learned to identify and infer the representation of disease-related areas by means of SSL, which contributes to performance and label efficiency in downstream tasks. On top of the reconstruction-based interpretation, we further used an advanced explanation tool (RELPROP42) to visualize the salient regions of images conducive to classifications made by fine-tuned models in downstream tasks (Extended Data Fig. 6b). For ocular disease diagnosis, well-defined pathologies were identified and used for classification, such as hard exudates and haemorrhage for diabetic retinopathy and parapapillary atrophy for glaucoma. For oculomic tasks, we observed that anatomical structures associated with systemic conditions, such as the optic nerve on CFP and nerve fibre layer and ganglion cell layer on OCT, were highlighted as areas that contributed to the incidence prediction of systemic diseases (Extended Data Fig. 6b).

This work introduces a new SSL-based foundation model, RETFound, and evaluates its generalizability in adapting to diverse downstream tasks. After training on large-scale unlabelled retinal images using an advanced SSL technique (masked autoencoder), RETFound can be efficiently adapted to a broad range of disease detection tasks, resulting in significant performance improvements for detecting ocular diseases and predicting cardiovascular and neurodegenerative diseases. It is a medical foundation model that has been developed and assessed, and shows considerable promise for leveraging such multidimensional data without constraints of enormous high-quality labels. 0852c4b9a8

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