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.

To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.

Training data are crucial for advancements in artificial intelligence, but many questions remain regarding the provenance of training datasets, license enforcement and creator consent. Mahari et al. provide a set of tools for tracing, documenting and sharing AI training data and highlight the importance for developers to engage with metadata of datasets.

The emergence of tools based on large language models (LLMs), such as OpenAI’s ChatGPT and Google’s Gemini, has garnered immense public attention owing to their advanced natural language generation capabilities. These remarkably natural-sounding tools have the potential to be highly useful for various tasks. However, they also tend to produce false, erroneous or misleading content—commonly referred

Google and OpenAI have recently announced major product launches involving artificial intelligence (AI) agents based on large language models (LLMs) and other generative models. Notably, these are envisioned to function as personalized ‘advanced assistants’. With other companies following suit, such AI agents seem poised to be the next big thing in consumer technology, with the potential to disrupt

Medical research is one of the most impactful areas for machine learning applications, but access to large and diverse health datasets is needed for models to be useful. Winning trust from patients by demonstrating that data are handled securely and effectively is key.

Recent years have witnessed the rise of commercialization efforts for soft robotics technology, which includes soft grippers, stretchable sensors and platforms for human–robot interactions. However, this commercialization lags behind the trends seen with other robotics technologies at equivalent points in their respective lifecycles. For example, the first patent for an industrial robotic manipulator