A Semi-Automated Solution Approach Selection Tool for Any Use Case via
  Scopus and OpenAI: a Case Study for AI/ML in Oncology
Deniz Kenan Kılıç Alex Elkjær Vasegaard Aurélien Desoeuvres Peter Nielsen
In today's vast literature landscape, a manual review is very time-consuming.
To address this challenge, this paper proposes a semi-automated tool for
solution method review and selection. It caters to researchers, practitioners,
and decision-makers while serving as a benchmark for future work. The tool
comprises three modules: (1) paper selection and scoring, using a keyword
selection scheme to query Scopus API and compute relevancy; (2) solution method
extraction in papers utilizing OpenAI API; (3) sensitivity analysis and
post-analyzes. It reveals trends, relevant papers, and methods. AI in the
oncology case study and several use cases are presented with promising results,
comparing the tool to manual ground truth.