  Nowadays, literature review is a necessary task when trying to solve a given
problem. However, an exhaustive literature review is very time-consuming in
today's vast literature landscape. It can take weeks, even if looking only for
abstracts or surveys. Moreover, choosing a method among others, and targeting
searches within relevant problem and solution domains, are not easy tasks.
These are especially true for young researchers or engineers starting to work
in their field. Even if surveys that provide methods used to solve a specific
problem already exist, an automatic way to do it for any use case is missing,
especially for those who don't know the existing literature. Our proposed tool,
SARBOLD-LLM, allows discovering and choosing among methods related to a given
problem, providing additional information about their uses in the literature to
derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises
three modules: (1: Scopus search) paper selection using a keyword selection
scheme to query Scopus API; (2: Scoring and method extraction) relevancy and
popularity scores calculation and solution method extraction in papers
utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and
post-analyzes which reveals trends, relevant papers and methods. Comparing the
SARBOLD-LLM to manual ground truth using precision, recall, and F1-score
metrics, the performance results of AI in the oncology case study are 0.68,
0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes
across various domains, showcasing its robustness and effectiveness. The
SARBOLD-LLM addresses engineers more than researchers, as it proposes methods
and trends without adding pros and cons. It is a useful tool to select which
methods to investigate first and comes as a complement to surveys. This can
limit the global search and accumulation of knowledge for the end user.
However...
