2026年10月23日 (英语).
Preeti Singh，技术程序管理员，领跑者，和Shravya Shetty，杰出软件工程师，Google研究
结核病仍然是世界上最致命的传染病之一，估计每年夺走123万人的生命。 尽管全球投资达几十年之久，但在负担最重的社区，仍有太多案件得不到发现。 在过去五年中，我们努力改变这一轨迹，从早期研究的基准测试转向开放的基础模型，这些模型可以在谨慎的时候调整和部署。
获得及时的医疗服务常常是一场地理事故：你住在哪里，你当地诊所拥有什么资源，专家是否在望。
这种差距在防治结核病方面最为严重。 尽管肺结核声称是可以预防和治疗的，但每年有123万多人的生命。 主要瓶颈是放射学：对于那些生活在高负担环境中的人来说，从生活在偏远农村地区的人们到在地下深处工作的矿工，放射科医生都很少，成像基础设施往往要等几天。 结果，数以百万计的活体病例得不到发现，使传播永久化。
在过去几年中，我们研究Google和卫生Google的团队努力改变这一轨迹：从一个基于AI的工具，这个工具以我们现有的医疗成像工作为基础，确定患有推定结核病的人进行后续测试，到未来的临床试验，与地方伙伴进行监管审查，并最终开放像MedGemma这样的基础模型，增强全球公共卫生系统的能力。 本周在华盛顿特区的IDWeek 2026，我们分享我们如何实现健康AI研究
我们的工作从一个根本性的问题开始：基于AI的工具能否以结核病筛查的专家放射学家的准确性来解释胸腔放射图？
胸射线学是肺部筛查的前沿成像方式，但检测到肺部渗入(肺部顶部浮液)和腹腔损伤(组织损伤)等肺结核的微妙迹象需要专业知识，而乡村诊所很少有这种知识。 2019年，我们发表了"放射学"中的基准测试，表明深层学习算法可以在性能水平上检测活性肺结核病，与有经验的放射学家匹配。
然而，现实世界的效用需要独立的核查。 2021年8月，Stop TB Partnership在The Lancet Digital Health对不同患者群的自动计算机辅助检测(CAD)软件进行了评价，将我们的模型排在全球顶级结核病筛查算法中。 当年，世卫组织还建议对结核病进行CAD筛查和分拣。 2024年9月，在NEJM AI中，我们详细列出了一个前景，多地点的验证，证明AI协助的筛选在不同的患者人群中保持了高度的敏感性(最小化失诊病例)，同时也可靠地检测出其他偶然的肺部和心脏异常。
在医学AI中，发表论文只是开始。 在算法模型和可信赖的床边工具之间，存在"翻译漏洞"：使软件在各种X射线机和离线设置中具有可靠性，将其融入临床工作流程，并满足医疗器械规范。
有鉴于此，我们与位于比勒陀利亚的一家医疗技术公司Nexus AI合作，他们将我们的胸X射线算法翻译成了认证的临床诊断工具。 需要进行广泛的审计，以了解监管环境：
- CE MDR IIb级认证：根据欧盟医疗器械条例(EU MDR 2017/745),Nexus AI实现了IIb级认证——这个严格指定可以验证该工具在未来的临床评估基础上对现实世界患者的护理是安全的。
- 质量标准：Nexus AI在医疗器械质量管理方面获得了对ISO 13485的认证遵守。
- 全球准入：Nexus AI与FIND签署了一项协议，以推行世界卫生组织资格预审(WHO PQ)，这是通过全球机构进行国际采购所必不可少的，同时也是美国林业发展局审批的一条持续途径。
今天，Nexus Intelligence正在积极应用这一技术，在最迫切需要诊断的地方。
Nexus AI的工具在大约40个保健设施运作。 迄今为止，已有90多名临床医生评估了25 000多张胸腔放射图，将周转时间从几周缩短到45秒以下，加快了快速分诊的速度，同时使保健专业人员有能力作出最后的临床决定。
我们的合作致力于保健公平：Nexus AI向资源不足的公共诊所提供了3 000多次免费检查，朝着每年10 000次赞助检查的目标迈进。
也许没有哪个部署能比在南非Mponeng金矿(世界上最深的矿场之一)所做的工作更能说明与病人见面的情况。 深沙矿工面临慢性硅灰尘暴露，肺病风险上升。 Nexus AI与制止结核病伙伴关系、和谐金矿和南非卫生部合作，在地下4公里部署超便携式数字X射线包和离线AI。 在几秒钟内对矿工进行现场筛选，并可以立即将标有推定结核病的矿工转诊进行确认分子试验。 筛选、分检和确认测试现在可以在一次访问中进行，压缩往往需要几周的时间。
单一伙伴的定制解决方案无法单独解决全球结核病负担。 为了扩大影响，我们从孤立的部署转向建立开放、适应性强的基础，使开发者和卫生部能够制定适合当地需要的解决办法。
通过健康AI开发者基金会(HAI-DEF)，我们开源我们的胸X射线嵌入和MedSigLIP，一个经过训练的轻量级视觉语言模型，将胸部X射线等医疗图像与用于描述的临床语言相配合。 由于MedSigLIP既能理解图像又能理解文本，开发者可以用它来用普通语言的标签对图像进行分类或搜索，或者用微调来完成一个只有少量本地实例的特定任务。 这使得当地团队可以在适度的区域数据集上建立专门的模型，在没有大量计算资源的情况下达到强效。
在此基础上，我们发布 MedGemma是一个开放量级的多式模型家族(4B和27B参数配置). MedGemma从基本分类发展到多模式临床推理：它用临床史综合射线学发现，用自然语言回答临床医生的询问，并在当地运行端侧，没有互联网接入。 自其发布以来，MedGemma在全世界的下载量已超过1000万次。
开放模式有助于公共卫生系统建立保护患者隐私的主权解决方案。 在东南亚，印度尼西亚卫生部正在应对世界第二高的结核病负担，其宏伟目标是每年对5 000万公民进行筛查，以根除这一疾病。
人工审查5 000万张X光是行不通的，专有云软件对数据居住构成挑战。 取而代之的是国内胸部放射图上的微调 MedGemma和MedSigLIP. 由于这些模型是在当地基础设施上运行的，病人数据仍留在国家边界内，使卫生领导人对临床部署拥有充分的主权。
Google研究认识到成像设备无法到达每个偏远的保健站，因此还开发了健康声波表示器。 在超过3亿个音频段的训练中，HEAR能够从标准智能手机麦克风记录的咳嗽声中进行非侵入声学筛选。
目前正通过在整个撒哈拉以南非洲的合作研究，与萨赫勒抗旱委员会(赞比亚)、Aurum研究所(加纳，得到250 000 Google.org赠款的支助)和HELINA的150万美元区域合作合作，利用热空方案。 声学分类法(HEAR)、移动X光检查法(HAI-DEF)和多模式临床推理法(MedGemma)共同为社区提供了服务不足人群的综合诊断工具级联。
从实验室算法到六个国家诊所使用的CE认证工具的旅程向我们展示了将研究转化为现实世界影响的所需条件：严格的科学、开放的开发者平台和专门的当地伙伴关系。 因为我们的伙伴最了解他们的社区，他们现在正进一步推动这项工作。 Nexus AI正在向新的国家扩展，印度尼西亚正在将结核病筛查纳入其国家方案，CIDRZ、Aurum研究所和HELINA正在建立基于咳嗽的分型的临床证据。 我们的作用是帮助他们获得开放模式、基础研究和持续支持，以便更快地诊断和治疗更多的肺结核患者。
在2026年IDWeek，我们期待着与传染病和公共卫生社区建立联系，共同努力扩大公平护理。
我们感谢我们在Nexus AI、Stop TB Partnership、项目厅、CIDRZ、Aurum Institute、HELINA、和谐金矿、南非卫生部和印度尼西亚卫生部的合作者，感谢他们临床领导和致力于全球卫生公平。
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本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
October 23, 2026
Preeti Singh, Lead, Technical Program Manager, and Shravya Shetty, Distinguished Software Engineer, Google Research
Tuberculosis remains one of the world's deadliest infectious diseases, claiming an estimated 1.23 million lives each year. Despite decades of global investment, too many cases go undetected in the communities that carry the highest burden. Over the past five years, we’ve worked to change that trajectory, moving from early research benchmarks toward open foundation models that could be tuned and deployed at the point of care.
Getting access to timely healthcare often comes down to an accident of geography: where you live, what resources your local clinic possesses, and whether a specialist is within reach.
Nowhere is this disparity more acute than in the fight against tuberculosis (TB). Despite being preventable and curable, TB claims more than 1.23 million lives annually. A primary bottleneck is radiology: For those in high-burden settings, from people who live in remote rural areas to miners working deep underground, radiologists are scarce, and imaging infrastructure is often days away. As a result, millions of active cases go undetected, perpetuating transmission.
Over the past several years, our team across Google Research and Google for Health has worked to change that trajectory: moving from an AI-based tool that builds on our existing work in medical imaging to identify people with presumptive TB for follow-up testing to prospective clinical trials, regulatory clearance with local partners, and ultimately open foundation models like MedGemma that empower public health systems worldwide. This week at IDWeek 2026 in Washington, D.C., we are sharing how we’re bringing health AI research to reality, and how foundational models can democratize TB screening at global scale.
Our work began with a fundamental question: Can AI-based tools interpret chest radiographs (CXRs) with the accuracy of an expert radiologist for TB screening?
Chest radiography is the frontline imaging modality for pulmonary screening, but detecting subtle signs of TB like apical infiltrates (fluid in the top portion of the lung) and cavitary lesions (tissue damage) requires specialized expertise that is rarely available in rural clinics. In 2019, we published a benchmark in Radiology, demonstrating that deep learning algorithms could detect active pulmonary TB at performance levels matching experienced radiologists.
Real-world utility, however, demands independent verification. In August 2021, the Stop TB Partnership evaluated automated computer-aided detection (CAD) software across diverse patient cohorts in The Lancet Digital Health, ranking our model among the top-performing TB screening algorithms globally. That year, WHO also recommended CAD for TB screening and triage. In September 2024, in NEJM AI, we detailed a prospective, multi-site validation demonstrating that AI-assisted screening maintains high sensitivity (minimizing missed cases) across diverse patient populations, while also reliably detecting other incidental pulmonary and cardiac abnormalities.
In medical AI, publishing a paper is only the beginning. Between an algorithmic model and a trusted bedside tool lies the "translation gap": making software reliable across varied X-ray machines and offline settings, integrating it into clinical workflows, and meeting medical device regulations.
With this in mind, we partnered with Nexus AI, a Pretoria-based medical technology company, and they translated our chest X-ray algorithms into a certified clinical diagnostic tool. Navigating the regulatory landscape required extensive audits:
- CE MDR class IIb certification: Under the European Union’s Medical Device Regulation (EU MDR 2017/745), Nexus AI achieved Class IIb certification — a rigorous designation that verifies the tool is safe for real-world patient care based on prospective clinical evaluations.
- Quality standards: Nexus AI obtained certified compliance with ISO 13485 for medical device quality management.
- Global access: Nexus AI signed an agreement with FIND to pursue the World Health Organization Pre-qualification (WHO PQ), which is essential for international procurement through global agencies, alongside an ongoing pathway toward the U.S. FDA clearance.
Today, Nexus Intelligence is actively deploying this technology where diagnostic access is most urgently needed.
Across six countries — South Africa, Sierra Leone, and Côte d'Ivoire, with expansions underway in the Democratic Republic of the Congo, Zambia, and Cameroon — Nexus AI’s tool operates in approximately 40 healthcare facilities. To date, over 90 clinicians have evaluated more than 25,000 chest radiographs, reducing turnaround times from weeks to under 45 seconds, accelerating rapid triage while empowering healthcare professionals to make final clinical decisions.
Our collaboration commits to health equity: Nexus AI has delivered more than 3,000 free screenings to under-resourced public clinics, progressing toward a goal of 10,000 sponsored screenings annually.
Perhaps no deployment better illustrates meeting patients where they are than the work done at the Mponeng Gold Mine in South Africa, one of the deepest mines in the world. Deep-shaft miners face chronic silica dust exposure and an elevated risk of pulmonary disease. In partnership with the Stop TB Partnership, Harmony Gold Mine, and the South African Department of Health, Nexus AI deployed ultra-portable digital X-ray kits and offline AI four kilometers underground. Miners are screened on-site within seconds, and those flagged with presumptive TB can be immediately referred for confirmatory molecular testing. Screening, triage and confirmatory testing can now happen in a single visit, compressing a pathway that often takes weeks.
A custom solution with a single partner cannot address the global TB burden alone. To scale impact, we shifted from isolated deployments to building open, adaptable foundations that allow developers and health ministries to create solutions tailored to local needs.
Through the Health AI Developer Foundations (HAI-DEF), we open-sourced our chest X-ray embeddings and MedSigLIP, a lightweight vision-language model trained to align medical images, such as chest X-rays, with the clinical language used to describe them. Because MedSigLIP understands both images and text, developers can use it to classify or search images with plain-language labels, or fine-tune it for a specific task with only a small set of local examples. This lets local teams build specialized models on modest regional datasets and reach strong performance without massive compute resources.
Building on this, we released MedGemma, a family of open-weight multimodal models (4B and 27B parameter configurations). MedGemma advances from basic classification to multimodal clinical reasoning: it synthesizes radiographic findings with clinical history, answers clinician queries in natural language, and runs locally on-device without internet access. Since its launch, MedGemma has seen more than 10 million downloads worldwide.
Open models help enable public health systems to build sovereign solutions that protect patient privacy. In Southeast Asia, the Ministry of Health of Indonesia is addressing the world's second-highest TB burden with an ambitious goal: screening 50 million citizens annually to eradicate the disease.
Reviewing 50 million X-rays manually is infeasible, and proprietary cloud software poses data residency challenges. Instead, the Ministry is fine-tuning MedGemma and MedSigLIP on domestic chest radiographs. Because the models run on local infrastructure, patient data remains within national borders, giving health leaders full sovereignty over clinical deployment.
Recognizing that imaging equipment cannot reach every remote health post, Google Research also developed Health Acoustic Representations (HeAR). Trained on over 300 million audio segments, HeAR enables non-invasive acoustic screening from cough sounds recorded on a standard smartphone microphone.
HeAR is being utilized through collaborative research across sub-Saharan Africa with CIDRZ (Zambia), the Aurum Institute (Ghana, supported by a $250,000 Google.org grant), and HELINA’s $1.5 million regional collaborative. Together, acoustic triage (HeAR), mobile X-ray screening (HAI-DEF), and multimodal clinical reasoning (MedGemma) offer communities an integrated diagnostic cascade of tools for underserved populations.
The journey from a laboratory algorithm to a CE-certified tool used in clinics across six countries has shown us what it takes to turn research into real-world impact: rigorous science, open developer platforms, and dedicated local partnerships. Because our partners know their communities best, they are now taking this work further. Nexus AI is expanding to new countries, Indonesia is integrating TB screening into its national program, and CIDRZ, the Aurum Institute and HELINA are building the clinical evidence for cough-based triage. Our role is to help equip them with open models, foundational research and ongoing support, so that more people with TB are diagnosed and treated sooner.
At IDWeek 2026, we look forward to connecting with the infectious disease and public health communities as we work together to expand equitable care.
We thank our collaborators at Nexus AI, Stop TB Partnership, UNOPS, CIDRZ, Aurum Institute, HELINA, Harmony Gold Mine, the South African Department of Health, and the Indonesian Ministry of Health for their clinical leadership and dedication to global health equity.
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Auto-fetched and translated by AI Atlas. All rights belong to the original author.