Opportunities and challenges of generative AI in the research lifecycle
TL;DR
An overview of AI’s role across the stages of scientific research is provided, illustrating how AI can assist researchers by aggregating and synthesizing a large body of work across various domains, supporting methodological implementation, and facilitating communication and publication.
Abstract
Artificial intelligence (AI) is increasingly being explored and adopted across the research lifecycle, from idea generation and literature discovery to data analysis, manuscript preparation, and editorial and peer-review processes. This perspective provides an overview of AI’s role across the stages of scientific research. We describe emerging tools and workflows, illustrating how AI can assist researchers by aggregating and synthesizing a large body of work across various domains, supporting methodological implementation, and facilitating communication and publication. We also discuss their shortcomings, including surface-level reasoning, the fabrication of plausible but incorrect outputs, and the challenges posed by the fact that researchers new to a field may not know which questions to ask or which nuances to interrogate. In addition, we discuss recent advances toward more agentic and end-to-end AI systems, highlighting both their technical feasibility and the challenges they pose for validation, oversight, and responsible use. For each stage of the research lifecycle, we outline key limitations of current AI systems and propose practical considerations, what researchers should and should not do to support rigorous and ethical integration of AI into scientific workflows. This integration requires coordinated frameworks across the ecosystem. Journals, funding agencies, universities, and policymakers play essential roles in defining standards for transparency and accountability, while individual researchers remain responsible for methodological rigor and validity of reported results.
