Balancing AI and Human Insights in Scientific Discovery: Challenges and Guidelines

Recent advancements in large language models (LLMs) have enabled AI systems to autonomously assist in scientific research, from hypothesis generation to laboratory experimentation, transforming how research proposals are written and experiments are designed. Tools like AI "co-scientists" promise to enhance scientific productivity but raise concerns about diminishing human intuition, reinforcing incremental research, and concentrating power among a few entities. As LLMs become increasingly integrated into research processes, there is a risk of reduced creativity, ethical misconduct, and overreliance on AI-driven evaluation systems. To address these challenges, in this article we propose ethical guidelines focusing on transparency, accountability, fairness, and safeguarding transformative research. Ultimately, AI should be used to augment—not replace—human insight in scientific discovery.n