Recent research on ray tracing cores has explored repurposing these cores to solve non-graphical problems by reformulating them as geometric queries, leveraging the inherent parallelism of ray tracing. Although successful in specific cases, these applications lack a clear pattern, and the conditions under which RT cores can provide computational benefits are still not clearly understood. The objective of this literature review is to examine diverse applications of ray tracing cores in general-purpose computation, identifying common features, performance gains, and limitations. By categorizing these efforts, the review aims to provide guidance on the types of problems that can effectively exploit ray tracing hardware beyond traditional rendering tasks. This is achieved with a blibliometric review based on 59 research articles indexed in Scopus, and a systematic literature review on 35 of them which propose new RT solutions and compare them with state-of-the-art methods to solve 32 distinct problems, in some works achieving up to $200\times$ speedup. Most of the problems analyzed in this work have applications in physics simulations and in solving some geometric queries, but problems with potential applications in databases and AI can also be found. Analyzing the characteristics of the problems, it was found that nearest neighbor search, including its variants, benefit the most from ray tracing cores as well as problems that rely on heuristic to diminish the necessary work. This is aligned with the biggest strength of RT cores; discarding tree branches when traversing a tree to avoid unnecessary work. Also, it was found that many short-length rays should be preferred over a few large rays. The results found in this work can serve as a guide for knowing beforehand which applications are better potential candidates to benefit from RT Core computation.