On the medical side, there are knowledgebases that offer clinical decision support like UpToDate (https://www.wolterskluwer.com/en/solutions/uptodate) that are kept up to date (pun intended) by specialists in their field. Every year or so, the articles are reviewed and updated with new information that has integrated into practice. For a relatively small fee, a practitioner has pretty much all access to the latest evidence-based standard of care across any specialty. UpToDate is also a commercial product. With a claimed 2+ million subscribers at roughly $200-500/yr, there is clearly money out there for a well made product.
In regards to the article, parsing academic publications and spitting out a word cloud or k-nn graphs of topics isn't going to be useful to a professional. They've already built up a working model in their mind that they've honed over the years. They have years of filtering information and the ones contributing to these knowledgebases have the experience to curate that information to professionals which is what's lacking from these NLP experiments.
I do think that ML and tools like SemanticScholar can be used to identify new literature that may affect knowledgebase articles and flag them for review. I'd be surprised if that doesn't already exists to some extent.
In regards to the article, parsing academic publications and spitting out a word cloud or k-nn graphs of topics isn't going to be useful to a professional. They've already built up a working model in their mind that they've honed over the years. They have years of filtering information and the ones contributing to these knowledgebases have the experience to curate that information to professionals which is what's lacking from these NLP experiments.
I do think that ML and tools like SemanticScholar can be used to identify new literature that may affect knowledgebase articles and flag them for review. I'd be surprised if that doesn't already exists to some extent.