A computer-implemented method comprising: obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model to generate a model output comprising a condition state score that represents a likelihood that the patient has a medical condition, wherein the fundus image processing machine learning model has been trained to process an input comprising one or more fundus images to generate a model output that represents a likelihood that a patient has the medical condition; wherein the fundus image processing machine learning model comprises a deep convolutional neural network comprising one or more initial convolutional layers configured to extract a respective feature vector for each of a plurality of regions in the fundus image, the one or more initial convolutional layers followed by an attention mechanism and subsequently followed by one or more additional neural network layers that are configured to receive an output of the attention mechanism and to process the attention output to generate the model output; wherein the attention mechanism is configured to: receive the respective feature vector for each of a plurality of regions in the fundus image generated by the one or more initial convolutional layers of the fundus image processing machine learning model, compute a respective attention weight for each of the regions, and generate the attention output by attending to the feature vectors in accordance with the attention weights for the regions in fundus image; and processing the model output to generate health analysis data for displaying in a user interface that comprises (i) the likelihood that the patient has the medical condition and (ii) data identifying the attention weights generated by the attention mechanism, wherein the data identifying the attention weights is an attention map that specifies the attention weights for the regions in the fundus image; and wherein the attention map is overlaid over the fundus image. obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model to generate a model output comprising a condition state score that represents a likelihood that the patient has a medical condition, wherein the fundus image processing machine learning model has been trained to process an input comprising one or more fundus images to generate a model output that represents a likelihood that a patient has the medical condition; wherein the fundus image processing machine learning model comprises a deep convolutional neural network comprising one or more initial convolutional layers configured to extract a respective feature vector for each of a plurality of regions in the fundus image, the one or more initial convolutional layers followed by an attention mechanism and subsequently followed by one or more additional neural network layers that are configured to receive an output of the attention mechanism and to process the attention output to generate the model output; wherein the attention mechanism is configured to: receive the respective feature vector for each of a plurality of regions in the fundus image generated by the one or more initial convolutional layers of the fundus image processing machine learning model, compute a respective attention weight for each of the regions, and generate the attention output by attending to the feature vectors in accordance with the attention weights for the regions in fundus image; and receive the respective feature vector for each of a plurality of regions in the fundus image generated by the one or more initial convolutional layers of the fundus image processing machine learning model, compute a respective attention weight for each of the regions, and generate the attention output by attending to the feature vectors in accordance with the attention weights for the regions in fundus image; and processing the model output to generate health analysis data for displaying in a user interface that comprises (i) the likelihood that the patient has the medical condition and (ii) data identifying the attention weights generated by the attention mechanism, wherein the data identifying the attention weights is an attention map that specifies the attention weights for the regions in the fundus image; and wherein the attention map is overlaid over the fundus image.