A new artificial intelligence system developed by Google can decide when to trust AI-based decisions about medical diagnoses and when to refer to a human doctor for a second opinion. Its creators claim it can improve the efficiency of analysing medical scan data, reducing workload by 66 per cent, while maintaining accuracy – but it has yet to be tested in a real clinical environment. The system, Complementarity-driven Deferral-to-Clinical Workflow (CoDoC), works by helping predictive AI know when it doesn't know something – heading off issues with the latest AI tools that can make up facts when they don't have reliable answers. It is designed to work alongside existing AI systems, which are often used to interpret medical imagery such as chest X-rays or mammograms. For example, if a predictive AI tool is analysing a mammogram, CoDoC will judge whether the perceived confidence of the tool is strong enough to rely on for a diagnosis or whether to involve a human if there is uncertainty.

HOW would you feel if your doctor, rather than consult their own clinical knowledge, turned instead to an AI trained on your medical history to help diagnose your next ailment or write your next prescription? These sorts of scenarios have been hypothetical for decades – the technology has been subpar and the stakes too high to risk offloading medical advice to a machine. However, the success of large language models like ChatGPT, a popular, artificially intelligent chatbot from the OpenAI research lab, has led to a rethink of what might be possible.

Could a robot do my job as a radiologist? If you asked me 10 years ago, I would have said, "No way!" But if you ask me today, my answer would be more hesitant, "Not yet -- but perhaps someday soon." In particular, new "deep learning" artificial intelligence (AI) algorithms are showing promise in performing medical work which until recently was thought only capable of being done by human physicians. For example, deep learning algorithms have been able to diagnose the presence or absence of tuberculosis (TB) in chest x-ray images with astonishing accuracy.