A new study from Harvard Medical School has found that artificial intelligence systems can outperform human doctors in emergency room triage, marking a major step forward in clinical decision-making technology.
The research, published in Science, showed that AI models diagnosed patients more accurately than physicians in high-pressure situations where rapid decisions are critical.
In one trial involving 76 emergency cases at a Boston hospital, an AI system correctly identified diagnoses in 67% of cases, compared with 50–55% accuracy among doctors. When given more detailed information, the AI’s accuracy rose to 82%, slightly ahead of human performance.
The AI model used in the study, OpenAI o1 reasoning model, also outperformed doctors in developing treatment plans, scoring 89% compared with 34% for physicians using conventional tools.
Researchers said the advantage was most evident in situations with limited data, where quick reasoning is essential.
Despite the results, experts stress that AI is not ready to replace doctors.
“I don’t think our findings mean that AI replaces doctors,” said Arjun Manrai, one of the study’s lead authors. “It does mean we’re witnessing a profound change that will reshape medicine.”
The study did not account for non-verbal cues such as patient appearance or distress, meaning the AI functioned more like a second-opinion tool based on written records.
Another lead researcher, Adam Rodman, said the future of healthcare could involve a “triadic” model combining doctor, patient, and AI system.
Independent experts welcomed the findings but warned about risks. Concerns include potential errors, lack of accountability, and the possibility that doctors may rely too heavily on AI recommendations.
There are also unanswered questions about how well AI performs across different patient groups, including the elderly and non-English speakers.
While adoption is already growing, analysts say the technology’s role is likely to expand as a support system rather than a replacement—helping doctors make better decisions, especially in complex or time-sensitive cases.
