Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science
This work addresses the problem of limited interdisciplinary collaboration in AI4Science for researchers, though it is incremental as it builds on existing qualitative efforts with a quantitative approach.
The authors tackled the gap between AI and scientific communities by conducting a large-scale literature analysis of AI4Science, using large language models to identify scientific problems and AI methods, and quantitatively highlighting disparities to reveal opportunities for deeper AI integration.
Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI methods in driving broad scientific discovery. Existing efforts in identifying and bridging this gap have often relied on qualitative examination of small samples of literature, offering a limited perspective on the broader AI4Science landscape. In this work, we present a large-scale analysis of the AI4Science literature, starting by using large language models to identify scientific problems and AI methods in publications from top science and AI venues. Leveraging this new dataset, we quantitatively highlight key disparities between AI methods and scientific problems, revealing substantial opportunities for deeper AI integration across scientific disciplines. Furthermore, we explore the potential and challenges of facilitating collaboration between AI and scientific communities through the lens of link prediction. Our findings and tools aim to promote more impactful interdisciplinary collaborations and accelerate scientific discovery through deeper and broader AI integration. Our code and dataset are available at: https://github.com/charles-pyj/Bridging-AI-and-Science.