Publishing papers in pure vs applied machine learning research
There are several questions in your post and it is really difficult to give a pin-pointed answer. So, I would go ahead and break your post into multiple segments and answer likewise:

... In pure Artificial Intelligence (AI) research one is required to come up with a new/improved algorithm and show that the algorithm has competitive performance across a set of toy or real data.

This premise is not entirely true. I work in AI (mostly in Machine Learning and Deep Learning) and we don't use the word like &quot;pure&quot; AI. It is just AI. If you really study history or inception of AI, it is more applied in some fashion. It is the fields like mathematics, probability and statistics that have helped in studies to be more focused around &quot;purity&quot; of it as such. In AI, you can come up with unique problems that cannot be solved using the present tools and techniques. That too is a great contribution. You don't always have to develop new algorithms.

Thus, I find this a well-specified target for pure AI research. Such research would be usually published in journals such as the Journal of Artificial Intelligence Research, Journal of Machine Learning Research.

These journals also publish applied research article, tools including software, and surveys. Of course, these journals and along with Machine Learning Journal by Springer are very well-known and oldest of their kinds and don't entertain papers which say &quot;Here is the dataset, here is the method we used, and here are the findings&quot;-type works. The contribution must be significant in so many different ways (be it a problem, a solution or a theory) that could be hugely contributing to the AI community.

I wonder how the target differs in applied AI research, such as machine learning (ML) applications in some domain, for instance, Health or Finance. Some example of such journals include Artificial Intelligence in Agriculture, Journal of Machine Learning in Finance, Artificial Intelligence in Medicine, Expert Systems with Applications.

The said journals are mainly problem or application-specific and they would like to see novel applications in their own fields. I don't have much experience with these journals. But, I certainly have reviewed papers for a few of these.

Do you have to come up with new/modified learning algorithms in order to publish in applied AI/ML journals?

As said earlier, not always.

If the answer to the above is no, then what is the nature of the contribution? Would it suffice to apply existing ML methods to new domain data?

My advice for you would be to read (and re-read) some papers from each of these journals and see what kind of works are valued there.

Suppose some existing methods are applied to new domain data. Considering the fact (model = algorithm + data) would it be acceptable to state &quot;this paper proposes a new model&quot; instead of &quot;a new learning algorithm&quot;?

&quot;Model = Algorithm + Data&quot; --- This is WRONG and whoever said this (even if it's not you) is WRONG!!! So your question does not stand. For your information, &quot;Model = Structure + Parameters&quot; (for starters, refer: Some papers from Bayesian ML)

What is the effective learning path that enables people to publish to some hypothetical journal &quot;Artificial Intelligence in X&quot; (where X is some other domain e.g. Finance or Medicine)? Are the same people supposed to hold BSc/MSc in both AI and X? Finally, are they supposed to already have published papers in either pure AI journals or X journals, before starting interdisciplinary research?

There are so many questions to answer. Please take my words with a bucket of salt: &quot;You need to do some basic and then some moderately advanced courses in ML to make your base adequately strong. Alternatively, get your hands dirty by solving problems in a platform such as Kaggle. In parallel, you could read some easy papers from the journals that you are interested in. Also, find a mentor who could guide you in these directions.