Thought Leadership

A study conducted by Professor Jian Gao from the Department of Social Work and Social Administration at The University of Hong Kong (HKU) and Professor Dashun Wang from Northwestern University reveals the significant impact of artificial intelligence (AI) on scientific research.  The study identifies a considerable gap between AI education and its practical application in research, indicating a mismatch between the supply of AI expertise and its demand.

Their findings show that AI is widely used across various scientific disciplines, with its adoption growing rapidly since 2015.  By analysing approximately 74.6 million academic papers across 19 disciplines and 292 fields published between 1960 and 2019, the study assessed AI’s role in shaping citation patterns and cross-discipline collaborations.  The research team discovered that papers mentioning AI-related terms — such as “machine learning” or “deep neural networks” — in their titles or abstracts were more likely to rank among the top 5% most-cited works within the same field and year.  These AI-linked papers also demonstrated broader cross-disciplinary influence, attracting citations beyond their primary research domains.

Additionally, the study highlights demographic disparities, showing that fields with higher proportions of women or Black scientists benefit less from AI, potentially exacerbating existing inequalities in science.  These insights have important implications for the fairness and sustainability of the research community as AI continues to become more integrated into scientific work.

This study was recently published in Nature Human Behaviour under the title “Quantifying the use and potential benefits of artificial intelligence in scientific research”.

Link to the paper: here