1) What short-term goals are you trying to achieve ? Recent AI advances have made it easier to transform unstructured information into structured one. But you'll still need to do some boring correspondence dataset construction. There is great value for a better research/cataloging of publication data (but probably not many people will pay for it).

Find what the users do with the data and make it easier for them using modern tools.

2) Medecine is a legal minefield. There is definitely a place for AI to cure illness and improve health. It's hard to get the data, and to standardized in it shape, but I'm convinced that just by the mere fact that it will have more personal data and vastly more general knowledge that AI generalist doctor will trounce any generalist doctor even with simple predictors. The real value is not to cure you when you are ill, but preventively to keep you healthy by continuous monitoring and adapted personal recommendation. Look at performance enhancing equipment for athletes optimal training for example.

There are plenty of things you can do with your graph database (probably more with the metadata than with the data) which can provide value, you can use it to make recommendations of paper to read (like arxiv sanity). Use it to connect researchers, for example if one of your co-paper author work with another researcher then you can suggest a work relation. You can cluster researchers. You can rank researchers according to a multitude of criterion (like an ELO score for researcher). You can create recommendation so that you reduce isolation between clusters and promote information dispersion.