Microsoft的AI诊断系统在模拟中正确诊断了复杂医疗病例，根据科学周四发表的一项研究，比通论医生多出一倍多。 Microsoft AI的首席执行官Mustafa Suleyman称这一结果为“医疗超级智能”的一步。
该系统被誉为MAI-DxO(简称DxO)，它协调了几个表现像医生组成的小组的AI agents. 他们质疑病人，命令测试，辩论诊断。
在一次利用新英格兰医学杂志的复杂病例的模拟中，83%的时间是正确的。 初级保健医生和一般实习医生单独使用参考材料，在36%的时间里是正确的。 其中一个案例是，通常由专家或医生团队处理
Microsoft AI健康副总裁Dominic King告诉TechCrunch, “目前提供保健服务的最大挑战是医疗专门知识的供求日益不匹配。”
在公布这些结果时，Microsoft 加入 Google和OpenAI声称自己的AI可以匹配或优于医生。
研究本身的数据显示，大部分改进来自更强大的基础AI模型，Microsoft的管弦系统增加了顶部的增益。
Microsoft用OpenAI,Google,Anthropic,Meta,DeepSeek等多家公司的型号测试了MAI-DxO，但其最好的结果是OpenAI的GPT-5和GPT-5.5.
Microsoft根据成本和准确度衡量了DxO，这意味着每个案件运行AI的估计费用。 GPT-5凭借一个短而基本的快速，在复杂案件中以每起案件约8400美元得分78.9%. DxO将这一成本削减了约3000美元，但融资精度仅减少约4点。 关于紧急部门案件，GPT-5在自行和DxO的协助下，在准确性或成本改进方面几乎没有什么改进。
随着GPT-5.5,2026年4月的更新型号发布，结果分化。 在复杂的案例中，DxO将GPT-5.5的精度分数从72%提高到近87%，成本略低一些。 不过，两者(有和没有DxO)都以类似成本得分约85%.
DxO在复杂案件上帮助最多，并且使用能力较差的AI模型。 例如，Gemini 2.5 Flash单独得分46.4%，但与DxO的得分接近69%。 即使如此，较弱和较强的型号之间的差距也大于所提供的助推DxO。
Microsoft的研究者称的精度随着模型接近理论极限而自然收缩。 发言人加上“DxO编织有助于应对优化准确性和成本这一更广泛的挑战”。
领导Microsoft建设自身前沿AI的Suleyman告诉TechCrunch，公司正与Mayo Clinic公司合作培训一个保健的基础模型，计划于明年发行。 他认为这个模式将使公司能够“在这里实现真正的巨大飞跃”，称 DxO是基础基础设施的一部分，Microsoft正在建设中，以看到真正的医疗突破。
“我确实相信，在2027年，我们将在医疗记录的编码风格上取得突破，”Suleyman 称。 “我认为我们将能够对医疗记录可能如何发展作出非常准确的预测，很明显，对临床医生的这种展望是金尘。 ”
苏莱曼的职业生涯一直试图利用AI来解决医疗问题。 他之前是DeepMind的联合创始人，2014年由Google担任收购。 次年，Google DeepMind 收购 King的启动程序Hark，一个临床任务管理应用，其中两个是发布 DeepMind Health一起。 苏莱曼于2024年加入Microsoft领导其AI单位，金在几个月后紧随其后。
“我们只是真正的信徒
他期望医疗模式能在未来24至36个月内提高能力
Suleyman 称说：「过去五六年，“飞轮意味着我们在生产中有一个模型，我们正与现实世界的用法相呼应，以便找出模型中的漏洞，然后加以补合和改进。 这就是编码中发生的情况。 之前在聊天时，我认为明年将开始为病历和临床决策提供支持。
Microsoft通过与波士顿贝丝以色列迪科内斯医疗中心的新的伙伴关系，将评价DxO在不同医院环境中的临床推理能力。
“这个技术已经准备好进行真实世界的评价，你可以想象周五晚上波士顿应急部门的问题的复杂性和多样性。”
他解释了研究将如何运作：病人将获得标准治疗，包括分诊、与专家的时间、治疗、调查和决定是否安全出院。 同时，同意者也将与DxO接触。 Microsoft及其伙伴将把DxO的建议与人类医生进行比较。
Google本月在The Lancet上与Beth Israel一同发表临床研究
称国王，“它不仅将跨越紧急情况，而且将跨越紧急护理环境，并将在研究期结束时向我们提供非常令人信服的证据，说明这些系统今后能够更大规模地部署。”
Microsoft在科学方面的研究与建立医疗超级智能所需要的一系列宣言相吻合，后者被定义为“在医疗推理和协调整个护理领域超越最强大的当代人类主导的卫生系统的能力 ”。 Microsoft希望超越个人任务的基准化——如回答体检问题和诊断——与临床医生和保健系统相结合，以取得更好的结果。
Suleyman 称补充道，与其他前沿实验室相比，这个焦点可能不同于Microsoft。
Microsoft已经向临床医生出售口述和放射学工具，并将DxO视为扩大这些努力。
“我们必须在今后几年内拥有绝对数第一的最先进医疗临床决策辅助产品”,Suleyman 称。
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Microsoft’s AI diagnostic system correctly diagnosed complex medical cases in a simulation more than twice as often as generalist physicians, according to a study published Thursday in Science. Mustafa Suleyman, Microsoft AI’s CEO, called the result a step toward “medical superintelligence.”
The system, dubbed MAI-DxO (DxO for short), coordinates several AI agents that act like a panel of doctors. They question a patient, order tests, and debate a diagnosis.
In a simulation using complex cases from the New England Journal of Medicine, it was correct 83% of the time. Primary care doctors and general internists, working individually with reference materials, were correct 36% of the time. One note: cases this complex are usually handled by specialists or physician teams, who weren’t part of the comparison.
“There is something very substantial and exciting here,” Dominic King, vice president of Health at Microsoft AI, told TechCrunch. “The biggest challenge in healthcare delivery at the moment is the growing mismatch between demand and supply of medical expertise.”
In publishing these results, Microsoft joins Google and OpenAI in claiming that its own AI can match or outperform doctors.
The study’s own data suggests that much of the improvement comes from more powerful underlying AI models, with Microsoft’s orchestration system adding gains on top.
Microsoft tested MAI-DxO with models from several companies, including OpenAI, Google, Anthropic, Meta, and DeepSeek, but its best results came with OpenAI’s GPT-5 and GPT-5.5.
Microsoft measured DxO on cost as well as accuracy, meaning the estimated cost of running the AI on each case. On its own with a short, basic prompt, GPT-5 scored 78.9% on complex cases at about $8,400 per case. DxO cut that cost by about $3,000 but raised accuracy by only about four points. On emergency department cases, there was little improvement in either accuracy or cost improvement between GPT-5 on its own and with the assist from DxO.
With GPT-5.5, a newer model released in April 2026, the results split. On complex cases, DxO increased GPT-5.5’s accuracy score from 72% to nearly 87% at a slightly cheaper cost. However, the gains once again didn’t show up on emergency department cases. Both (with and without DxO) scored about 85% at a similar cost.
DxO helps most on complex cases and with less capable AI models. Gemini 2.5 Flash, for example, scored 46.4% on its own but nearly 69% with DxO. Even so, the gap between the weaker and stronger models was larger than the boost DxO provided.
Microsoft’s researchers said that accuracy gains naturally shrink as models approach the theoretical maximum. “The DxO orchestration helps with the broader challenge of optimization both accuracy and cost,” a spokesperson added.
Suleyman, who leads Microsoft’s effort to build its own frontier AI, told TechCrunch that the company is working with Mayo Clinic to train a foundation model for healthcare, with release planned for next year. He believes this model will enable the company to “make really enormous leaps here,” and says DxO is part of the foundational infrastructure Microsoft is building to see real breakthroughs in healthcare.
“I really believe that in 2027, we will be on the verge of a coding style breakthrough for medical records,” Suleyman said. “I think we’re going to be able to stream highly accurate predictions about how a medical record might unfold, and obviously that kind of foresight for a clinician is gold dust.”
Suleyman has spent his career trying to use AI to solve medical issues. He was previously a co-founder of DeepMind, which was acquired by Google in 2014. The following year, Google DeepMind acquired King’s startup Hark, a clinical task management app, and the two of them launched DeepMind Health together. Suleyman joined Microsoft to lead its AI unit in 2024, and King followed a few months later.
“We’re just true believers,” Suleyman said.
He said while the use of generative AI in healthcare is still early, he expects healthcare models to get “an order of magnitude better in capability within the next 24 to 36 months.”
“As we’ve seen multiple times in the last five or six years, the progress happens very quickly once you have a flywheel,” Suleyman said. “A flywheel means we have a model in production which we are iterating with real world usage in order to identify gaps in the model and then patch them and improve them. That’s what’s happened in coding. It’s what happened in chat before that. And I think next year it’s going to start to happen for medical records and for clinical decision support.”
Through a new partnership with Beth Israel Deaconess Medical Center in Boston, Microsoft will evaluate the clinical reasoning capabilities of DxO across different hospital settings.
“This technology is ready for real world evaluations, and you can imagine the complexity and diversity of issues that come from a Boston emergency department on a Friday evening,” King said.
He explained how the study will work: patients will get standard treatment, including triage, time with a specialist, treatments, investigations, and decision on whether it’s safe to discharge. In parallel, those who consent will also engage with DxO. Microsoft and its partners will compare DxO’s recommendations against the human doctors’.
Google published a clinical study alongside Beth Israel in The Lancet this month to evaluate Google’s AMIE conversational diagnostic AI in a real-world urgent care visits, while human physicians monitored the interactions in real time.
“We think this will be the biggest study of its kind,” King said. “It will be across not just emergency settings but urgent care environments, and will give us very compelling evidence by the end of the study period around the ability for these systems to be deployed at greater scale in the future.”
Microsoft’s study in Science coincides with a manifesto of sorts around what it will take to build medical superintelligence, defined as “the capacity to surpass the strongest contemporary human-led health systems in medical reasoning and coordination across the full arc of care.” Microsoft hopes to move beyond benchmarking individual tasks — like answering medical exam questions and generating diagnoses — into integrating with clinicians and health care systems for better outcomes.
“This is the kind of technology that everybody in the world wants, and that’s why we’re committed to doing everything we can to accelerate its introduction to the world,” Suleyman said, adding that this focus could be a differentiator for Microsoft compared to the other frontier labs.
Microsoft already sells dictation and radiology tools to clinicians and sees DxO as an expansion to those efforts.
“We have to have the absolute number one state-of-the-art medical clinical decision support products over the next few years,” Suleyman said.
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