The rapid convergence of artificial intelligence (AI) with scientific research, often referred to as AI for Science (AI4Science), is reshaping the landscape of discovery across disciplines. Clarifying current progress and identifying promising pathways forward is essential to guide future development and unlock AI's transformative potential in scientific research. By analyzing AI-related research in leading natural and health science journals, we assess AI's integration into scientific fields and highlight opportunities for further growth. While AI's role in high-impact research is expanding, broader adoption remains hindered by cognitive and methodological gaps, necessitating targeted interventions to address these challenges. To accelerate AI4Science, we propose three key directions: equipping experimental scientists with user-friendly tools, developing proactive AI researchers within scientific workflows, and fostering a thriving AI-Science ecosystem. Additionally, we introduce a structured AI4Science workflow to guide both experimental scientists and AI researchers in leveraging AI for discovery, while proposing strategies to overcome adoption barriers. Ultimately, this work aims to drive broader AI integration in research, advancing scientific discovery and innovation across disciplines.

翻译：人工智能（AI）与科学研究的快速融合，通常被称为“人工智能赋能科学”（AI4Science），正在重塑各学科领域的发现格局。阐明当前进展并确定有前景的未来路径，对于指导未来发展、释放AI在科学研究中的变革潜力至关重要。通过分析顶尖自然科学与健康科学期刊中与AI相关的研究，我们评估了AI在科学领域的整合程度，并指出了进一步发展的机遇。尽管AI在高影响力研究中的作用正在扩大，但认知与方法论上的差距仍阻碍着其更广泛的采用，这需要采取有针对性的干预措施来应对这些挑战。为加速AI4Science的发展，我们提出三个关键方向：为实验科学家配备用户友好的工具、在科学工作流中培养主动型AI研究者，以及培育繁荣的AI-科学生态系统。此外，我们引入了一个结构化的AI4Science工作流，以指导实验科学家和AI研究者利用AI进行科学发现，同时提出了克服采用障碍的策略。最终，本工作旨在推动AI在研究中更广泛的整合，从而促进跨学科的科学发现与创新。