A study conducted by\u00a0Professor Jian Gao from the Department of Social Work and Social Administration at The University of Hong Kong (HKU)\u00a0and\u00a0Professor Dashun Wang from Northwestern University reveals the significant impact of artificial intelligence (AI) on scientific research.\u00a0 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.<\/p>\n
Their findings show that AI is widely used across various scientific disciplines, with its adoption growing rapidly since 2015.\u00a0 By analysing approximately 74.6 million academic papers\u00a0across\u00a019 disciplines and 292 fields\u00a0published between 1960 and 2019, the study assessed AI\u2019s role in shaping citation patterns and cross-discipline collaborations.\u00a0 The research team discovered that papers mentioning AI-related terms \u2014 such as\u00a0\u201cmachine learning\u201d\u00a0or\u00a0\u201cdeep neural networks\u201d \u2014 in their titles or abstracts were\u00a0more likely\u00a0to rank among the top 5% most-cited works within the same field and year. \u00a0These AI-linked papers also demonstrated broader cross-disciplinary influence, attracting citations beyond their primary research domains.<\/p>\n
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. \u00a0These insights have important implications for the fairness and sustainability of the research community as AI continues to become more integrated into scientific work.<\/p>\n
This study was recently published in\u00a0Nature Human Behaviour<\/em>\u00a0under the title\u00a0\u201cQuantifying the use and potential benefits of artificial intelligence in scientific research\u201d.<\/p>\n