Key points are not available for this paper at this time.
The factors contributing to different levels of artificial intelligence (AI) adoption by librarians and their patrons need clarifying in the context of literature reviews. This paper addresses this need by exploring the transformative impact of AI on literature reviews, particularly within academic librarianship in the health sciences. Drawing on literature and professional experience, it examines how AI is reshaping reviews, potentially extending their meaning beyond text-based sources to accommodate multimedia content and predictive insights. While highlighting AI's promise in enhancing research efficiency and comprehensiveness, the paper also notes the lack of documentation of AI's uptake for literature reviews, perhaps reflecting concerns over reliability and biases. Proposed strategies for moving forward include matching different literature reviews with the most appropriate AI systems. This alignment guides librarians and researchers in navigating the complexities of AI adoption, using human oversight to ensure the integrity and quality of AI content. The paper underscores the importance of education, training, and continuous consultation to promote trustworthy and responsible AI utilization. This pathway foresees more robust outcomes from literature reviews in domains like health care in the digital age.
Buetow et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: