Could AI Help Reform Academic Publishing?

As someone whose work crosses so many disciplines, I spend a fair bit of my days skimming new developments across not only computer science, but the humanities, social sciences, arts and many other fields, looking for connections and unexpected new approaches that might benefit my own work. The intensely siloed nature of academia is well known, but equally striking is just how rapidly citation standards are falling in a Google Scholar world filled with explosive growth in available knowledge, in which scholars seem genuinely unaware of developments across the rest of their own field, not to mention the rest of academia. Could machine learning approaches dramatically reform the "related work" and citation review component of peer review and academic publishing? Perhaps the most striking element of modern scholarship is that in an era when much of our modern scholarship is available through web and academic database searches, it takes only a few mouse clicks to compile a cross-section of the recent developments in a given space. Yet, peruse the "related work" or "background" section of a typical academic paper and it is amazing just how discipline-specific and artificially circumscribed the set of references are.