As I was reading, a couple of big, critical questions kept popping into my head. They’re less about small typos and more about the core approach of the study itself.
The paper does a great job of reviewing a ton of research, but I’m struggling to see the “there” there. It reads more like a really thorough introduction to a research program rather than a study that presents a new, synthesized finding. What is the single, most important takeaway or novel conclusion that your analysis of all these papers has uncovered? How does this review move the field forward in a way that a simple list of “what we know” doesn’t?
My biggest concern is about the methodology. You describe this as a “narrative review,” but then you make some very strong, prescriptive claims about how computational models “will” and “should” be used for diagnosis and treatment. How can you make such definitive claims without a systematic methodology? Without clear inclusion/exclusion criteria or a quality assessment of the reviewed papers, aren’t you at serious risk of cherry-picking the evidence that supports your viewpoint while overlooking studies that might show the limitations or failures of these computational approaches?