We need a new paradigm to think about generative AI

Numerous studies have now shown that large language models (LLMs) have remarkable similarities to physician cognition in silico, showing both human-like—or even superhuman—performance on cognitive medical tasks as well as reflecting human biases.1–6 Reasoning models, which were publicly introduced in September 2024, have since become ubiquitous in commercial products such as ChatGPT (GPT-5, OpenAI) and Gemini (Gemini 3, Google). They use chain-of-thought processing during model inference, allowing them to decompose complex clinical scenarios into intermediate steps, verify logic, correct errors prior to providing output and drastically increase their performance on a variety of cognitive tasks. The recent piece by Wang and Redelmeier in this issue of BMJ Quality and Safety shows that these powerful models, similar to the base models that preceded them, continue to show human cognitive biases when tested on clinical vignettes.7