Wei Xing

Nature has published two independent studies demonstrating the ability of large language models based on artificial intelligence (AI) to support different stages of patient management in controlled settings. The first study analysed MIRA, an AI agent that operates within electronic health records, which achieved a diagnostic accuracy of nearly 88%, compared with 78% for a panel of physicians. The second study evaluated AMIE, a conversational clinical reasoning model, against 21 primary care physicians across 100 multi-visit scenarios. AMIE achieved performance comparable to, and in some cases better than, that of physicians in terms of treatment accuracy, test ordering, and adherence to clinical guidelines. The models are based on simulations or retrospective data, which limits the strength of the conclusions that can be drawn. The findings are consistent with another model published in Science last April.

The use of artificial intelligence (AI) in medical diagnosis centres on computing and data processing. Research published in Science assesses the diagnostic capabilities of an advanced large language model, which managed to match or outperform human professionals. The team carried out six experiments involving both standardised clinical cases and a study using real cases from emergency department records, using the performance of hundreds of doctors as a benchmark. The AI proved particularly useful in situations of uncertainty, such as the initial stages of triage in the emergency department. However, the authors highlight that the model only processed text, whereas clinical practice also relies on visual and auditory cues.