The potential role of AI in research priority setting exercises

Abstract

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.

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