面向给定用例的半自动化解决方案推荐器：基于Scopus与OpenAI的肿瘤学AI/ML案例研究
A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI
AI总结：
针对文献综述耗时长、方法筛选难的问题，提出由Scopus检索、GPT 3.5方法提取评分、分析三大模块构成的SARBOLD-LLM工具，可快速推荐用例解决方案，肿瘤学案例验证其F1值达0.77，适用于工程领域的方法初筛。
摘要：
如今，在尝试解决特定问题时，文献综述是一项必要工作。但在当下海量的文献格局中，开展详尽的文献综述非常耗时，即便仅查找摘要或综述类文献，也可能耗费数周时间。此外，从众多方法中选出合适的一种，且在相关的问题与解决方案领域内定向检索，都并非易事。对于刚进入所在领域的年轻研究者或工程师而言，情况尤其如此。尽管目前已有针对特定问题提供解决方法的综述，但仍缺乏一种可适配任意用例的自动化实现方式，尤其对不了解现有文献的人群来说更是如此。\n我们提出的工具SARBOLD-LLM（基于大语言模型的半自动化解决方案推荐器）能够在短短几小时内，发现并筛选与给定问题相关的方法，同时提供这些方法在文献中的应用情况等额外信息，以生成决策参考。SARBOLD-LLM包含三个模块：（1：Scopus检索）采用关键词选择方案查询Scopus API，实现论文筛选；（2：评分与方法提取）利用OpenAI API（GPT 3.5）计算相关性与流行度评分，并提取论文中的解决方案方法；（3：分析）开展敏感性分析与后续分析，揭示研究趋势、相关论文及方法。\n通过精确率、召回率和F1值指标，将SARBOLD-LLM与人工标注的基准真值进行对比，在肿瘤学人工智能案例研究中，其性能结果分别为0.68、0.9和0.77。SARBOLD-LLM在多个领域均取得了成功效果，展现出其鲁棒性与有效性。该工具更面向工程师而非研究者，因为它仅提供方法与趋势，未附加优缺点分析。它是一款可用于筛选优先研究方法的实用工具，可作为综述的补充，能够减少终端用户的全局检索量与知识积累负担。
英文摘要：
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...
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
