Strategies for integrating ChatGPT and generative AI into clinical studies.

IF 2.3 Q2 HEMATOLOGY Blood Research Pub Date : 2024-12-24 DOI:10.1007/s44313-024-00045-3
Jeong-Moo Lee
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引用次数: 0

Abstract

Large language models, specifically ChatGPT, are revolutionizing clinical research by improving content creation and providing specific useful features. These technologies can transform clinical research, including data collection, analysis, interpretation, and results sharing. However, integrating these technologies into the academic writing workflow poses significant challenges. In this review, I investigated the integration of large-language model-based AI tools into clinical research, focusing on practical implementation strategies and addressing the ethical considerations associated with their use. Additionally, I provide examples of the safe and sound use of generative AI in clinical research and emphasize the need to ensure that AI-generated outputs are reliable and valid in scholarly writing settings. In conclusion, large language models are a powerful tool for organizing and expressing ideas efficiently; however, they have limitations. Writing an academic paper requires critical analysis and intellectual input from the authors. Moreover, AI-generated text must be carefully reviewed to reflect the authors' insights. These AI tools significantly enhance the efficiency of repetitive research tasks, although challenges related to plagiarism detection and ethical use persist.

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将ChatGPT和生成式人工智能整合到临床研究中的策略。
大型语言模型,特别是ChatGPT,通过改进内容创建和提供特定的有用功能,正在彻底改变临床研究。这些技术可以改变临床研究,包括数据收集、分析、解释和结果共享。然而,将这些技术集成到学术写作工作流程中带来了重大挑战。在这篇综述中,我研究了将基于大语言模型的人工智能工具整合到临床研究中,重点关注实际实施策略,并解决与使用相关的伦理问题。此外,我提供了在临床研究中安全可靠地使用生成人工智能的例子,并强调需要确保人工智能生成的输出在学术写作环境中可靠有效。总之,大型语言模型是有效组织和表达思想的强大工具;然而,它们也有局限性。撰写学术论文需要作者的批判性分析和智力投入。此外,必须仔细审查人工智能生成的文本,以反映作者的见解。这些人工智能工具显著提高了重复性研究任务的效率,尽管与剽窃检测和道德使用相关的挑战仍然存在。
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来源期刊
Blood Research
Blood Research HEMATOLOGY-
CiteScore
3.70
自引率
0.00%
发文量
64
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