Harnessing artificial intelligence for efficient systematic reviews: A case study in ecosystem condition indicators

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-09-10 DOI:10.1016/j.ecoinf.2024.102819

Isabel Nicholson Thomas , Philip Roche , Adrienne Grêt-Regamey

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引用次数: 0

Abstract

Effective evidence synthesis is important for the integration of scientific research into decision-making. However, fully depicting the vast mosaic of concepts and applications in environmental sciences and ecology often entails a substantial workload. New Artificial Intelligence (AI) tools present an attractive option for addressing this challenge but require sufficient validation to match the vigorous standards of a systematic review. This article demonstrates the use of generative AI in the selection of relevant literature as part of a systematic review on indicators of ecosystem condition. We highlight, through the development of an optimal prompt to communicate inclusion and exclusion criteria, the need to describe ecosystem condition as a multidimensional concept whilst also maintaining clarity on what does not meet the criteria of comprehensiveness. We show that, although not completely infallible, the GPT-3.5 model significantly outperforms traditional literature screening processes in terms of speed and efficiency whilst correctly selecting 83 % of relevant literature for review. Our study highlights the importance of precision in prompt design and the setting of query parameters for the AI model and opens the perspective for future work using language models to contextualize complex concepts in the environmental sciences. Future development of this methodology in tandem with the continued evolution of the accessibility and capacity of AI tools presents a great potential to improve evidence synthesis through gains in efficiency and possible scope.

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利用人工智能进行高效的系统审查：生态系统状况指标案例研究

有效的证据综合对于将科学研究融入决策非常重要。然而，要充分描述环境科学和生态学中的各种概念和应用往往需要大量的工作量。新的人工智能（AI）工具为应对这一挑战提供了一个极具吸引力的选择，但需要充分的验证才能与系统综述的严格标准相匹配。本文展示了生成式人工智能在选择相关文献中的应用，作为生态系统状况指标系统综述的一部分。我们通过开发一个最佳提示来传达纳入和排除标准，强调了将生态系统状况描述为一个多维概念的必要性，同时也明确了哪些内容不符合全面性标准。我们的研究表明，尽管 GPT-3.5 模型并非完全无懈可击，但它在速度和效率方面明显优于传统的文献筛选流程，同时还能正确选择 83% 的相关文献进行审查。我们的研究强调了人工智能模型在提示设计和查询参数设置方面精确性的重要性，并为今后使用语言模型对环境科学中的复杂概念进行语境化处理的工作开辟了前景。随着人工智能工具的可及性和能力的不断发展，这种方法的未来发展将为通过提高效率和扩大可能的范围来改进证据合成带来巨大的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.