Artificial-intelligence (AI) tools are becoming increasingly common in science, and many scientists anticipate that they will soon be central to the practice of research, suggests a Nature survey of more than 1,600 researchers around the world.

Science and the new age of AI: a Nature special

When respondents were asked how useful they thought AI tools would become for their fields in the next decade, more than half expected the tools to be ‘very important’ or ‘essential’. But scientists also expressed strong concerns about how AI is transforming the way that research is done.

The share of research papers that mention AI terms has risen in every field over the past decade, according to an analysis for this article by Nature.

Machine-learning statistical techniques are now well established, and the past few years have seen rapid advances in generative AI, including large language models (LLMs), that can produce fluent outputs such as text, images and code on the basis of the patterns in their training data. Scientists have been using these models to help summarize and write research papers, brainstorm ideas and write code, and some have been testing out generative AI to help produce new protein structures, improve weather forecasts and suggest medical diagnoses, among many other ideas.

With so much excitement about the expanding abilities of AI systems, Nature polled researchers about their views on the rise of AI in science, including both machine-learning and generative AI tools.

Focusing first on machine-learning, researchers picked out many ways that AI tools help them in their work. From a list of possible advantages, two-thirds noted that AI provides faster ways to process data, 58% said that it speeds up computations that were not previously feasible, and 55% mentioned that it saves scientists time and money.

“AI has enabled me to make progress in answering biological questions where progress was previously infeasible,” said Irene Kaplow, a computational biologist at Duke University in Durham, North Carolina.

The survey results also revealed widespread concerns about the impacts of AI on science. From a list of possible negative impacts, 69% of the researchers said that AI tools can lead to more reliance on pattern recognition without understanding, 58% said that results can entrench bias or discrimination in data, 55% thought that the tools could make fraud easier and 53% noted that ill-considered use can lead to irreproducible research.

“The main problem is that AI is challenging our existing standards for proof and truth,” said Jeffrey Chuang, who studies image analysis of cancer at the Jackson Laboratory in Farmington, Connecticut.

Essential uses

To assess the views of active researchers, Nature e-mailed more than 40,000 scientists who had published papers in the last 4 months of 2022, as well as inviting readers of the Nature Briefing to take the survey. Because researchers interested in AI were much more likely to respond to the invitation, the results aren’t representative of all scientists. However, the respondents fell into 3 groups: 48% who directly developed or studied AI themselves, 30% who had used AI for their research, and the remaining 22% who did not use AI in their science. (These categories were more useful for probing different responses than were respondents’ research fields, genders or geographical regions; see Supplementary information for full methodology).

Among those who used AI in their research, more than one-quarter felt that AI tools would become ‘essential’ to their field in the next decade, compared with 4% who thought the tools essential now, and another 47% felt AI would be ‘very useful’. (Those whose research field was already AI were not asked this question.) Researchers who don’t use AI were, unsurprisingly, less excited. Even so, 9% felt these techniques would become ‘essential’ in the next decade, and another 34% said they would be ‘very useful’.

Large language models

The chatbot ChatGPT and its L