How do scientists use Artificial Intelligence in their research? The impact of AI tools in Nature’s survey on user-friendly artificial intelligence products

Around half of the scientists in the survey said that there were barriers preventing them from developing or using AI as much as they would like — but the obstacles seem to be different for different groups. The researchers who directly studied AI were most concerned about a lack of computing resources, funding for their work and high-quality data to run AI on. Those who work in other fields but use AI in their research tended to be more worried by a lack of skilled scientists and training resources, and they also mentioned security and privacy considerations. Researchers who didn’t use AI generally said that they didn’t need it or find it useful, or that they lacked experience or time to investigate it.

Researchers who regularly use LLMs at work are still in a minority, even among the interested group who took Nature’s survey. Some 28% of those who studied AI said they used generative AI products such as LLMs every day or more than once a week, 13% of those who only use AI said they did, and just 1% among others, although many had at least tried the tools.

Survey respondents told us, for example, that they are using AI to process data, write code and help them write papers. One clear benefit for many is in English-language science communication. Researchers who do not use English for their first language can userative-ai tools to publish their research, since they use large language models. Scientists can use LLMs to improve their writing skills and to translate other people’s work.

Meanwhile, AI has also been changing. Whereas the 2010s saw a boom in the development of machine-learning algorithms that can help to discern patterns in huge, complex scientific data sets, the 2020s have ushered in a new age of generative AI tools pre-trained on vast data sets that have much more transformative potential.

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.

Artificial intelligence has enabled me to answer biological questions that have been previously infeasible, said a computational biologist at Duke University.

Jeffrey Chuang is a scientist at the Jackson Laboratory who studies image analysis of cancer.

People said they were worried about false information and bias if the tools were trained to look at biased data. Scientists have seen evidence of this: a team in the United States reported, for instance, that when they asked the LLM GPT-4 to suggest diagnoses and treatments for a series of clinical case studies, the answers varied depending on the patients’ race or gender (T. Zack et al. Preprint at medRxiv https://doi.org/ktdz; 2023) — probably reflecting the text that the chatbot was trained on.

“There is clearly misuse of large language models, inaccuracy and hollow but professional-sounding results that lack creativity,” said Isabella Degen, a software engineer and former entrepreneur who is now studying for a PhD in using AI in medicine at the University of Bristol, UK. “In my opinion, we don’t understand well where the border between good use and misuse is.”

The clearest benefit, researchers thought, was that LLMs aided researchers whose first language is not English, by helping to improve the grammar and style of their research papers, or to summarize or translate other work. The academic community can demonstrate how to use these tools for good, even if there is a small number of malicious players.

The most popular use of all groups was for artistic and fun activities unrelated to research, a small share used the tools to write code, and another used to help write research papers.

Artificial Intelligence is the New Language: Towards More Benefit Than Issues for Drug Discovery in Bioinformatics and Biomolecular Medicine

A chemist at the University of Oxford who works on software for drug discovery believes that the principles of LLMs can be used to build similar models in bioinformatics, but they must be large. “Only a very small number of entities on the planet have the capabilities to train the very large models — which require large numbers of GPUs [graphics processing units], the ability to run them for months, and to pay the electricity bill. That constraint is limiting science’s ability to make these kinds of discoveries,” he says.

There was a split among respondents if journal editors and peer reviewers could adequately review papers that used artificial intelligence. About half the scientists that used AI for their work didn’t use it directly, while one quarter thought reviews were adequate and one quarter thought they were not. People who developed artificial intelligence had more positive opinions of the review processes.

Several researchers said that artificial intelligence and postgraduate linguistics were here to stay. “AI is transformative,” wrote Yury Popov, a specialist in liver disease at the Beth Israel Deaconess Medical Center in Boston, Massachusetts. “We have to focus now on how to make sure it brings more benefit than issues.”