AI 模型和平台
Microsoft 诊断性AI 超常医生科学研究
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Microsoft的MAI-DxO诊断系统在2026年10月22日发表于"科学"的同行评审研究中，在复杂诊断病例上表现优于参与的通论医生，该研究在新英格兰医学杂志的病例记录上报告系统的准确度为83.2%，而使用参考资源的医生为36.1%。
研究"用语言模型进行相应诊断"出现在"科学"第394卷，第6822期第423–428页；2026年4月4日收到，2026年8月10日接受。 所有15位作者都是Microsoft员工，名单中包括MicrosoftAI首席执行官穆斯塔法·苏莱曼与多米尼克·金和埃里克·霍维茨并列。 在同一天的公告中，Microsoft 称的作品"今天发表在科学"杂志的"AI in Medicine"期刊上。
基准测试围绕一步一步的诊断构建
论文推出序列诊断基准测试，即SD Bench，将304个新英格兰医学杂志临床病理学会诊病例和325个紧急部门对入境的遭遇从MIMIC-IV-ED数据集转换成互动的，逐步的诊断遭遇。 每个病例都以短暂的临床阴道开始，诊断师，无论是医生还是人工智能系统，都可以从中询问历史或检查问题，命令诊断测试，或者承诺进行最终诊断。 根据诊断的准确性与参考诊断和所订购的测试的累计估计成本对性能进行评分。
每逢遇上两个支持agents结构。 一个守门员agent根据基本案例记录对每条信息请求进行调解，并在请求的发现缺失时产生诊断中性合成结果，因此缺失的数据不会发出答案信号。 一名法官agent将承诺的诊断与参考诊断放在一个5级医生授权的标题上，得分为4或5，代表的是管理层不变的诊断，算作正确。 测试费用是使用根据Kaiser Permanente Washington中央医院标准收费文件得出的基于CPT的定价估算的，历史和检查问题折叠成固定的医生访问费用。
在基准测试内部，作者评价了MicrosoftAI诊断Orchestrator，在论文中缩写为DxO，是一种模拟具有互补作用的临床专家小组的模型-不可知论系统，在每个步骤上应用成本意识的测试指导，综合诊断跨独立运行。 兹Q 1Z的公告形容这个系统就像一个虚拟医生小组，提交人认为，静态基准测试，它预先提供所有案件资料，并要求一个单一的答案，不能衡量一个系统是否提出正确的问题，避免不加区分的测试，或承认它是否有足够的确定性来完成一项工作。
NEJM和紧急部门案件的结果
在临床病理会病例中，DxO与GPT-5配对，运行时没有预算限制，达到83.2%的精度，平均每例费用为5,431美元。 成本约束配置精度达到80.9%，为2 241美元，79.6%为1 303美元，而旨在诊断精度的配置精度达到84.5%，为8 945美元。 在对56个因工程工程而停产的病例进行随附的医生研究中，参与者进行了764次封闭式诊断和662次开放式诊断，平均22.3%的准确性封闭式诊断为每例3 049美元，36.1%的封闭式诊断为4 725美元。 单身最佳医生占51.8%。 论文还报道了不同的工作风格：医生在订购第一次测试前提出了平均5.6个问题，而DxO和GPT-5则提出了平均25.5个问题，作者认为这是一种广泛的结构化历史采集策略。
在紧急部门个案中，没有预算限制的DxO达到了80.3%的准确度，每例4 979美元，在预算限制下达到73.2%2 634美元。 一般医生平均为46.8%，为3 400美元，最佳个人医生为62.5%。 在以最低基线提示进行测试的所有模型中，DxO协调融资诊断精度平均为会议个案7.9个百分点，紧急情况部门个案4.4个百分点。 兹Q 1Z的公告指出，没有任何其他评估人类或AI方法同时比MAI-DxO更准确，成本也更低，称该系统在急诊部门演示中准确度是医生的1.7倍，比医生在期刊上使用教科书和互联网的准确度高一倍以上，比医生在没有这些资源的情况下诊断准确度高近四倍，为费用的一半至三分之一。
DxO最初是在GPT-4.1是现有最强模型时开发的，后来用GPT-5来评价，完整的评价套件在发布前与GPT-5.5重复使用，没有修改提示或调谐，其精度优势高于基线提示。 更早的作品版本于2025年6月27日作为预印版上传，以OpenAI的o3模型报告80%的准确度，是医师平均20%的4倍，最高准确度配置85.5%；同行评审版本新增，应急部门病例集和GPT-5.5更新评价。
说明限制和作者披露
提交人指出，这些比较并不显示优于专家、多学科小组或现实世界的临床实践，并写道，他们期望与每一案例相匹配的专家或小组相比，业绩差距会缩小。 他们指出，所衡量的优势是在一种可控制的模拟中放大的，这种模拟将不确定的病人历史、不完整的文件、关系、纵向跟踪以及成像和病理幻灯片等多式联运数据放在一边，而且期刊案例可能过于具有诊断挑战性。 另一项说明的限制是比较个别医生而不是多专业协作，法官评价疾病一级的诊断正确性，而不是治疗的适当性、安全性或纵向管理。
在论文的披露中，作者指出，他们都是在公司持有股份的Microsoft雇员，几位是Microsoft专利申请的发明者，涉及一个按顺序诊断的自动评价框架，研究没有Microsoft以外的资金是收购。
开源释放和临床伙伴关系
Microsoft 称是SD Bench的开源软件，用于选择NEJM案件，包括基准测试，守门员，法官，以及成本估算代码。 在麻省理工学院的许可下，该代码可在GitHub上提供，其配置名称是 " 科学 "，该配置将门卫和判官钉在纸上。 根据SD Bench项目网站，期刊案例衍生的数据集需要NEJM Group颁发许可证，MIMIC子集通过PhysioNet发布，并接受认证，5艘全合成案例船作为默认开放数据集。
推出医疗情报的六个相互依存的维度，并声明超越医疗超智能的门槛要求“超越超人执行孤立任务，Microsoft的公告称，论文发表于BMJ数字健康与AI. 该公司还在称将来自Microsoft各地的卫生团队，包括Microsoft研究科学家和龙Copilot和PowerScribe背后的团队，纳入Microsoft AI的一体化努力，并提及其先前的宣布 Mayo诊所合作；根据2026年6月2日的公告，各组织正在开发马约诊所将拥有的前沿AI保健模型，Microsoft计划通过Azure Foundary API提供。
Microsoft 称通过与波士顿贝丝以色列迪科内斯医疗中心(Beth Israel Deaconess Medical Center, Microsoft 称)的新伙伴关系，它将在一项未来研究中评价各医院环境的临床推理能力，其中病人直接与系统互动，并且根据医生的独立评估来衡量表现。
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AI Models & Platforms
Microsoft Diagnostic AI Outperforms Generalist Physicians in Science Study
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Microsoft’s MAI-DxO diagnostic system outperformed participating generalist physicians on complex diagnostic cases in a peer-reviewed study published October 22, 2026 in Science, which reported 83.2% accuracy for the system on New England Journal of Medicine case records against 36.1% for physicians using reference resources.
The study, “Sequential diagnosis with language models,” appears in Science Volume 394, Issue 6822, pages 423–428; it was received April 4, 2026 and accepted August 10, 2026. All 15 authors are Microsoft employees, and the list includes Microsoft AI chief executive Mustafa Suleyman alongside Dominic King and Eric Horvitz. In a same-day announcement, Microsoft said the work “is published today in Science” in the journal’s “AI in Medicine” issue.
A Benchmark Built Around Step-by-Step Diagnosis
The paper introduces the Sequential Diagnosis Benchmark, or SDBench, which converts 304 New England Journal of Medicine clinicopathological conference cases and 325 emergency department-to-admission encounters from the MIMIC-IV-ED dataset into interactive, stepwise diagnostic encounters. Each case opens with a brief clinical vignette, from which the diagnostician, whether a physician or an AI system, can ask history or examination questions, order diagnostic tests, or commit to a final diagnosis. Performance is scored on diagnostic accuracy against the reference diagnosis and on the cumulative estimated cost of the tests ordered.
Two supporting agents structure each encounter. A Gatekeeper agent mediates every information request against the underlying case record and generates diagnostically neutral synthetic results when a requested finding is absent, so missing data does not signal the answer. A Judge agent scores the committed diagnosis against the reference diagnosis on a five-level physician-authored rubric, with scores of 4 or 5, representing diagnoses for which management would be unchanged, counted as correct. Test costs were estimated using CPT-based pricing derived from the Kaiser Permanente Washington Central Hospital Standard Charges file, with history and examination questions folded into a fixed physician-visit cost.
Within the benchmark, the authors evaluated the Microsoft AI Diagnostic Orchestrator, abbreviated DxO in the paper, a model-agnostic system that simulates a panel of clinical experts with complementary roles, applies cost-conscious test stewardship at each step, and aggregates diagnoses across independent runs. Microsoft’s announcement describes the system as operating like a panel of virtual physicians debating over several rounds. The authors argue that static benchmarks, which present all case information upfront and ask for a single answer, cannot measure whether a system asks the right questions, avoids indiscriminate testing, or recognizes when it has enough certainty to conclude a workup.
Results on NEJM and Emergency-Department Cases
On the clinicopathological conference cases, DxO paired with GPT-5 and run without budget constraints achieved 83.2% accuracy at an average cost of $5,431 per case. Cost-constrained configurations reached 80.9% accuracy at $2,241 and 79.6% at $1,303, while a configuration aimed at diagnostic excellence reached 84.5% at $8,945. In the accompanying physician study, conducted on 56 cases held out from engineering work, participants produced 764 closed-book and 662 open-book diagnoses, averaging 22.3% accuracy closed-book at $3,049 per case and 36.1% open-book at $4,725. The single best physician reached 51.8%. The paper also reports differing workup styles: physicians asked a mean of 5.6 questions before ordering their first test, while DxO with GPT-5 asked a mean of 25.5, a strategy the authors describe as extensive structured history-taking.
On the emergency-department cases, DxO without budget constraints achieved 80.3% accuracy at $4,979 per case and 73.2% at $2,634 under budget constraints. Generalist physicians averaged 46.8% at $3,400, and the best individual physician reached 62.5%. Across all models tested with minimal baseline prompts, DxO orchestration raised diagnostic accuracy by an average of 7.9 percentage points on the conference cases and 4.4 points on the emergency-department cases. Microsoft’s announcement states that no other evaluated human or AI approach was simultaneously more accurate and less costly than MAI-DxO, describing the system as 1.7 times more accurate than physicians on the emergency-department presentations, more than twice as accurate as physicians using textbooks and the internet on the journal cases, and nearly four times as accurate as physicians diagnosing without those resources, at half to one third of the cost.
DxO was initially developed when GPT-4.1 was the strongest model available, was subsequently evaluated with GPT-5, and the full evaluation suite was repeated with GPT-5.5 before publication without modifying prompts or orchestration, with its accuracy advantage over the baseline prompt increasing. An earlier version of the work was posted as a preprint on June 27, 2025, reporting 80% accuracy with OpenAI’s o3 model, four times the 20% physician average, and 85.5% in a maximum-accuracy configuration; the peer-reviewed version adds the emergency-department case set and updated evaluations with GPT-5 and GPT-5.5.
Stated Limitations and Author Disclosures
The authors state that the comparisons do not show superiority over specialists, multidisciplinary teams, or real-world clinical practice, and write that they would expect the performance gap to narrow against expert specialists or teams matched to each case. They note the measured advantages are amplified within a controlled simulation that sets aside uncertain patient histories, incomplete documentation, rapport, longitudinal follow-up, and multimodal data such as imaging and pathology slides, and that the journal cases may overrepresent diagnostically challenging presentations. A further stated limitation is the comparison against individual physicians rather than multispecialty collaboration, and the Judge rubric evaluates diagnostic correctness at the disease level rather than therapeutic appropriateness, safety, or longitudinal management.
In the paper’s disclosures, the authors state that all of them are Microsoft employees holding equity in the company, that several are inventors on a Microsoft patent application related to an automated evaluation framework for sequential diagnosis, and that no funds external to Microsoft were acquired for the studies.
Open-Source Release and Clinical Partnerships
Microsoft said it is open-sourcing SDBench for select NEJM cases, including the benchmark, Gatekeeper, Judge, and cost-estimation code. The code is available on GitHub under the MIT License, with a configuration named “science” that pins the gatekeeper and judge used for the paper’s results. According to the SDBench project site, the dataset derived from the journal cases requires licensing from NEJM Group, the MIMIC subset is distributed through PhysioNet subject to credentialing, and five fully synthetic cases ship as the default open dataset.
A companion position paper, “Medical Superintelligence: Foundations and Aspirations,” introduces six interdependent dimensions of medical intelligence and states that crossing the threshold to medical superintelligence will require “more than superhuman performance on isolated tasks,” namely systems that surpass today’s strongest human-led health systems in integrated medical reasoning across the full arc of care. Microsoft’s announcement states that the paper was published in BMJ Digital Health and AI. The company also said it is bringing health teams from across Microsoft, including Microsoft Research scientists and the teams behind Dragon Copilot and PowerScribe, into an integrated effort at Microsoft AI, and it referenced its previously announced Mayo Clinic collaboration; under a June 2, 2026 announcement, the organizations are developing a frontier AI model for healthcare that Mayo Clinic will own and that Microsoft plans to make available through Azure Foundry APIs.
Through a new partnership with Beth Israel Deaconess Medical Center in Boston, Microsoft said it will evaluate clinical reasoning capabilities across hospital settings in a prospective study in which patients interact with the system directly and performance is measured against physicians’ independent assessments.
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