结核病仍是全球最致命的传染病之一，每年估计导致123万人死亡。尽管数十年来全球持续投入，但在疾病负担最重的社区中，仍有大量病例未被发现。过去五年间，我们致力于改变这一现状，从早期研究基准逐步转向可在诊疗现场进行微调与部署的开放基础模型。

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.

结核病仍是全球最致命的传染病之一，每年夺去约 123 万人的生命。尽管全球已投入数十年资源，但在疾病负担最重的社区中，仍有过多病例未被发现。过去五年间，我们致力于改变这一轨迹，从早期研究基准逐步转向可在诊疗现场进行微调与部署的开放基础模型。能否及时获得医疗服务往往取决于地理环境的偶然性：你居住在哪里、当地诊所拥有何种资源，以及专家是否触手可及。在抗击结核病（TB）的过程中，这种不平等尤为严峻。尽管结核病可预防且可治愈，但每年仍导致超过 123 万人死亡。放射学是主要瓶颈之一：对于高负担地区的人群——无论是居住在偏远农村的居民，还是在地下深处作业的矿工——放射科医生稀缺，影像基础设施往往远在数日行程之外。因此，数百万活动性病例未被检出，导致传播持续不断。 我们的工作始于一个根本性问题：基于人工智能的工具在结核病筛查方面，能否以媲美专家放射科医生的准确度解读胸部 X 光片（CXR）？胸部 X 光检查是肺部筛查的一线影像手段，但检测结核病的细微征象（如肺尖浸润和空洞病变）需要专业专长，而这在农村诊所中极少具备。2019 年，我们在《Radiology》期刊发表了一项基准测试，证明深度学习算法检测活动性肺结核的性能水平可与经验丰富的放射科医生相匹敌。 在医疗 AI 领域，发表论文仅仅是开始。从算法模型到值得信赖的床旁工具之间存在着“转化鸿沟”：使软件在不同型号的 X 光机和离线环境中保持可靠，将其整合进临床工作流程，并符合医疗器械法规要求。Nexus AI 设备可在短短 45 秒内对胸部 X 光片进行筛查，以检测包括结核病在内的肺部疾病。如今，Nexus Intelligence 正积极将这项技术部署在诊断需求最为迫切的地区。我们的合作承诺致力于健康公平：Nexus AI 已向资源匮乏的公立诊所提供了超过 3,000 次免费筛查，并朝着每年提供 10,000 次资助筛查的目标迈进。 仅靠与单一合作伙伴定制的解决方案无法独自应对全球结核病负担。为了扩大影响力，我们从孤立部署转向构建开放、可适应的基础设施，使开发者和卫生部门能够创建符合本地需求的解决方案。Nexus AI 的应用程序通过分析集成模型的胸部 X 光片，突出显示与正常肺野不一致的发现，包括提示结核病的迹象，以及需要进一步调查（FIR）的情况。在 IDWeek 2026 上，我们期待与传染病和公共卫生界建立联系，共同推动公平护理的扩展。

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. 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. 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. The Nexus AI device screens chest X-rays for lung diseases like TB in as little as 45 seconds. Today, Nexus Intelligence is actively deploying this technology where diagnostic access is most urgently needed. 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. 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. Nexus AI’s application analyzes a chest X-ray integrating models, which highlights findings that are not consistent with normal lung fields, including findings suggestive of tuberculosis, and when further investigation is required (FIR). At IDWeek 2026, we look forward to connecting with the infectious disease and public health communities as we work together to expand equitable care.