Artificial intelligence (AI) and robotics are increasingly positioned as the future of healthcare, promising to transform diagnosis, treatment, and patient care. However, there is a lack of evidence showing that AI-powered systems can reliably deliver in high-pressure clinical settings, where accuracy and speed are critical.
In an attempt to examine the performance of large language models (LLMs) in medical contexts, particularly a real-life emergency room, a new study has found that at least one LLM was able to diagnose patients more accurately than human doctors. It provided the exact or very close diagnosis in 67 per cent of cases, compared to human doctors with 50-55 per cent accuracy rate.
The study was published in Science journal last week by a team of researchers comprising physicians and computer scientists at Harvard Medical School and Beth Israel Deaconess Medical Center.
The findings of the study, based on trials that tested the responses of hundreds of doctors against LLMs, suggest that AI-powered systems may be inching closer to supporting doctors in real-world decision-making. It comes at a time when AI adoption in healthcare is picking up, with nearly one in five US physicians already using AI to assist diagnosis, as per the American Medical Association (AMA).
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