Garry John, Tomlinson Mark, Lohan Maria
Queen's University Belfast, Department of Politics and International Relations, Northern Ireland, UK.
Stellenbosch University, Institute for Life Course Health Research, Cape Town, South Africa.
J Glob Health. 2025 Jun 6;15:03019. doi: 10.7189/jogh.15.03019.
To help achieve the goals of accountability and research excellence, funding organisations often utilise evidence from research priority setting exercises (RPSEs), which distil, from data gathered from relevant stakeholders, a systematic and 'objective' rank-order of research priorities. RPSEs are, however, costly and labour-intensive. Also, critics of RPSEs have highlighted certain limitations: insufficient representation of difficult-to-reach stakeholders, especially in low- and middle-income countries; a lack of genuine stakeholder engagement; wide variation in the extent to which exercises are documented; a lack of specificity in the identified priorities; and minimal impact of the priorities. Artificial intelligence (AI) tools such as ChatGPT may potentially help, valuably complementing conventional RPSEs. While the opacity of AI decision-making is a limitation, advantages include speed, affordability, and highly inclusive distillation of the vastness of existing human knowledge. We encourage research identifying the extent to which AI can replicate conventional RPSEs. We suggest that AI tools could complement conventional approaches either at the initial question generation stage or in generating supplementary insights for reflection at the data analysis stage. Also, under conditions of high existing stakeholder engagement and an extant prevalence of conventional RPSEs, AI-only studies may be valuable.
为了有助于实现问责制和卓越研究的目标，资助机构经常利用研究优先级设定活动（RPSEs）的证据，这些活动从相关利益相关者收集的数据中提炼出研究优先级的系统且“客观”的排序。然而，RPSEs成本高昂且劳动强度大。此外，RPSEs的批评者强调了某些局限性：难以接触到的利益相关者代表性不足，尤其是在低收入和中等收入国家；缺乏真正的利益相关者参与；活动记录程度差异很大；确定的优先级缺乏具体性；以及优先级的影响微乎其微。诸如ChatGPT之类的人工智能（AI）工具可能会有所帮助，对传统的RPSEs起到有价值的补充作用。虽然人工智能决策的不透明性是一个局限性，但其优点包括速度快、成本低，以及对现有海量人类知识进行高度包容性的提炼。我们鼓励开展研究，以确定人工智能能够在多大程度上复制传统的RPSEs。我们建议，人工智能工具可以在最初的问题生成阶段补充传统方法，或者在数据分析阶段为反思提供补充性见解。此外，在现有利益相关者参与度高且传统RPSEs普遍存在的情况下，仅使用人工智能的研究可能会有价值。
