Does the test set is used to update weight in a deep learning model with keras?
No , you souldn't use your test set for training to prevent overfitting  ,  if you use cross-validation principles you need exactly to split your data into three datasets  a train set which you'll use to train your model , a validation set to test different value of your  hyperparameters , and a test set to finally test your model , if you use all your data for training, your model will overfit obviously.
One thing to remember deep learning work well if you have a large and very rich datasets
I'm wondering if the result of the test set is used to make the optimization of model's weights. I'm trying to make a model but the issue I have is I don't have many data because they are medical research patients. The number of patient is limited in my case (61) and I have 5 feature vectors per patient. What I tried is to create a deep learning model by excluding one subject and I used the exclude subject as the test set. My problem is there is a large variability in subject features and my model fits well the training set (60 subjects) but not that good the 1 excluded subject. 
So I'm wondering if the testset (in my case the excluded subject) could be used in a certain way to make converge the model to better classify the exclude subject?