ChatGPT於系統性文獻回顧和統合分析之應用

在系統性文獻回顧（systematic review, SR）和統合分析（meta-analysis, MA）使用聊天生成式預訓練轉換器（chat generative pre-trained transformer, ChatGPT）作為輔助工具需要審慎，建立嚴格的品質管制與驗證機制，以減少AI（artificial intelligence）「幻覺（hallucinations）」可能帶來的錯誤。研究顯示，在某些任務，如文獻篩選和資訊提取中，ChatGPT的表現可媲美甚至超越人類專家。但是，在執行複雜任務，如偏差風險評估方面，其表現仍有明顯侷限，顯示人類專家角色之重要性。本文回顧ChatGPT在SR和MA中的應用現況、潛在風險，並提出人機協作上的建議。在使用ChatGPT輔助研究時，需考慮不同情境任務，採取最適合的作法。在研究報告中保持使用ChatGPT的透明度以維護研究誠信，並且需要關注道德規範，包括數據隱私、偏見與公平性問題。最後，本文針對ChatGPT的應用角度提出建議，強調以人類為核心觀點下，在生成式人工智慧（artificial intelligence generated content）時代來臨時，應該著重培養的能力，包括持續自我迭代能力、提示詞工程技能能力、批判性思考能力、跨學科協作能力，以及倫理素養。以期在合理合規下，能持續優化人機協作模式，提高ChatGPT等AI工具在複雜任務中的表現，達到使用創新技術提升效率並具備科學嚴謹性之目標。

The current uses, potential risks, and practical recommendations for using chat generative pre-trained transformers (ChatGPT) in systematic reviews (SRs) and meta-analyses (MAs) are reviewed in this article. The findings of prior research suggest that, for tasks such as literature screening and information extraction, ChatGPT can match or exceed the performance of human experts. However, for complex tasks such as risk of bias assessment, its performance remains significantly limited, underscoring the critical role of human expertise. The use of ChatGPT as an adjunct tool in SRs and MAs requires careful planning and the implementation of strict quality control and validation mechanisms to mitigate potential errors such as those arising from artificial intelligence (AI)‘hallucinations’. This paper also provides specific recommendations for optimizing human-AI collaboration in SRs and MAs. Assessing the specific context of each task and implementing the most appropriate strategies are critical when using ChatGPT in support of research goals. Furthermore, transparency regarding the use of ChatGPT in research reports is essential to maintaining research integrity. Close attention to ethical norms, including issues of privacy, bias, and fairness, is also imperative. Finally, from a human-centered perspective, this paper emphasizes the importance of researchers cultivating continuous self-iteration, prompt engineering skills, critical thinking, cross-disciplinary collaboration, and ethical awareness skills with the goals of: continuously optimizing human-AI collaboration models within reasonable and compliant norms, enhancing the complex-task performance of AI tools such as ChatGPT, and, ultimately, achieving greater efficiency through technological innovative while upholding scientific rigor.