Researchers urgently need more guidance to help them identify and mitigate potential risks when designing projects that involve AI developments.

Clear descriptions of intelligence in both living organisms and machines are essential to avoid confusion, sharpen thinking and guide interdisciplinary research. A Comment in this issue encourages researchers to answer key questions to improve clarity on the terms they use.

Tackling partial differential equations with machine learning solvers is a promising direction, but recent analysis reveals challenges with making fair comparisons to previous methods. Stronger benchmark problems are needed for the field to advance.

The EU Artificial Intelligence Act bans certain “subliminal techniques beyond a person’s consciousness”, but uses undefined legal terms. Interdisciplinary efforts are needed to ensure effective implementation of the legal text.

The incorporation of artificial intelligence (AI) into genetics and genomics research can enable research that would have been otherwise impossible. However, these benefits must be considered together with the potential risks to humans, other sentient beings, and the environment. Genetic and genomic advances require much trial and error to succeed; this is ethically fraught when the consequences are

The 2024 Nobel prizes in physics and chemistry highlight the interdisciplinary nature and impact of AI in science.

On 12 September 2024, OpenAI released two new large language models (LLMs) — o1-preview and o1-mini — marking an important shift in the competitive landscape of commercial LLMs, particularly concerning their reasoning capabilities. Since the introduction of GPT-3.5, OpenAI has launched 31 LLMs in two years. Researchers are rapidly applying these evolving commercial models in clinical medicine, achieving

In this Comment, we propose a cumulative set of three essential criteria for the ethical use of LLMs in academic writing, and present a statement that researchers can quote when submitting LLM-assisted manuscripts in order to testify to their adherence to them.

A new class of AI models, called foundation models, has entered healthcare. Foundation models violate several basic principles of the standard machine learning paradigm for assessing reliability, making it necessary to rethink what guarantees are required to establish warranted trust in them.

Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.

Machine learning (ML) is driving exciting innovations in drug discovery, but we need to be mindful of the circumstances that set this application apart. Unlike other fields with fit-for-purpose datasets consisting of millions of examples, published datasets in drug discovery are classically heterogeneous, imbalanced, noisy and expensive to generate1. Furthermore, the applications of ML in drug discovery

New multi-modal AI methods fuse different biological data types that span multiple scales, offering promising clinical utility.