2024 | Katy E. Trinkley, Ruopeng An, Anna M. Maw, Russell E. Glasgow, Ross C. Brownson
This paper explores the potential of artificial intelligence (AI) to advance implementation science (IS), addressing key challenges such as speed, sustainability, equity, generalizability, context-outcome relationships, and causality. AI can enhance the efficiency and effectiveness of IS methods, but it also poses potential pitfalls, including the risk of exacerbating inequities and introducing biases. The authors provide examples from global health systems, public health, and precision health to illustrate both the advantages and hazards of integrating AI into IS. They recommend transdisciplinary collaboration and proactive monitoring to ensure responsible and ethical use of AI in IS. The paper concludes with a call for increased uptake of AI innovations in IS, emphasizing the need for vigilance to avoid unintended consequences.This paper explores the potential of artificial intelligence (AI) to advance implementation science (IS), addressing key challenges such as speed, sustainability, equity, generalizability, context-outcome relationships, and causality. AI can enhance the efficiency and effectiveness of IS methods, but it also poses potential pitfalls, including the risk of exacerbating inequities and introducing biases. The authors provide examples from global health systems, public health, and precision health to illustrate both the advantages and hazards of integrating AI into IS. They recommend transdisciplinary collaboration and proactive monitoring to ensure responsible and ethical use of AI in IS. The paper concludes with a call for increased uptake of AI innovations in IS, emphasizing the need for vigilance to avoid unintended consequences.