Oliveira, ArlindoAcademia das Ciências de Lisboa2025-12-052025-12-052025-12-05http://hdl.handle.net/10400.26/60272Generative artificial intelligence has significantly impacted scientific research by enabling new methods for data analysis, hypothesis generation, and experimental design. These technologies have accelerated discoveries in fields such as drug development, materials science, and climate modeling by automating the generation of novel solutions and simulations. By enhancing data-driven insights, generative AI has also improved the accuracy and efficiency of predictive models, opening new avenues for interdisciplinary research. However, the use of artificial intelligence raises ethical concerns regarding data integrity, reproducibility, fake results, and the potential for bias, highlighting the need for responsible and transparent implementation in scientific processes and workflows. This article addresses several of these issues, using as the starting point the SAPEA evidence review report on the successful and timely uptake of artificial intelligence in science.engtext