It sounds like a very interesting project. Our experience so far with transfer learning from ImageNet models to large medical images has been very poor. You lose too much information going from high-resolution grayscale to low-resolution RGB. There are certainly some applications where it makes sense, but it doesn’t seem like radiography has many of them. I referenced some of the approaches we tried for CT and transfer learning in Black and white images on VGG16
If you look at models for segmentation like U-NET, they were trained entirely with medical / histological data and perform quite well.
As with most of these new areas, the only way to really know is to test it out. It would probably be a good idea to define a project or even make a test dataset (Kaggle is a great place to upload them and get feedback from others, https://www.kaggle.com/kmader/siim-medical-image-analysis-tutorial). Since you mentioned age and body part, it might be an interesting challenge to see if training a CNN to classify a large number of different body parts, makes it better at estimating age of a specific body part.