Through AI-driven intelligent healthcare, bioinformatics, and ethical AI.
从分子到人体，让 AI 读懂生命 —— 多模态健康数据 × 前沿 AI 引擎 × 疾病全周期管理 × Ethical AI 底座。
From molecules to humans: multimodal health data × frontier AI engines × full-cycle disease management, all grounded on Ethical AI.
01 · Data Layer 数据层
多模态 · 多尺度健康数据 Multimodal · Multi-scale Health Data
从人群与器官，到细胞与分子 —— 生命信号跨越多个数量级，我们将宏观临床世界与微观分子宇宙汇聚到同一个可计算空间。
From populations and organs down to cells and molecules — we bring the macro clinical world and the micro molecular universe into one computable space.
CONVERGENCE
数据汇聚 = 可计算的生命 Convergence = Computable Life
影像像素、病历文本、心电波形、基因序列 —— 异构数据经过统一表征学习（Representation Learning）映射到共享语义空间，使跨尺度的推理成为可能：一个突变如何改变蛋白构象，如何扰动细胞状态，最终如何在影像上留下可见的痕迹。
Imaging pixels, clinical text, ECG waveforms and gene sequences — heterogeneous data are mapped into a shared semantic space via Representation Learning, enabling cross-scale reasoning: how a mutation reshapes a protein, perturbs cell states, and finally leaves a visible trace on an image.
LIVE SIGNAL · ECG
streaming02 · AI Engine 技术引擎
AI 算法与系统层 AI Algorithms & Systems
数据之上，是推理的机器。点击切换引擎核心，查看每类模型在医疗场景中的能力。
Above the data sits the reasoning machine. Click to switch between engine cores.
Medical LLM
读懂医学语言的推理核心 A reasoning core fluent in medical language
在医学文献、临床指南与真实病历语料上训练的大语言模型，具备医学知识推理、病历结构化、报告生成与循证问答能力。
Trained on medical literature, clinical guidelines and real EHR corpora, with capabilities in medical reasoning, record structuring, report generation and evidence-based QA.
病历自动结构化与质控 EHR structuring & QC
循证医学问答与文献溯源 Evidence-based QA with citation tracing
影像报告草拟与摘要生成 Radiology report drafting
Multimodal Medical FM
跨越像素、文本与波形的统一表征 Unified representations across pixels, text and waveforms
将影像、病理切片、检验指标、时序信号与文本映射到同一语义空间的基座模型，支持跨模态检索、零样本迁移与多源证据融合。
A foundation model mapping imaging, pathology slides, lab tests, time-series signals and text into one semantic space — enabling cross-modal retrieval, zero-shot transfer and multi-evidence fusion.
影像-报告跨模态对齐与检索 Image-report alignment & retrieval
病理全切片（WSI）智能分析 Whole-slide image analysis
组学-表型跨尺度关联发现 Omics-phenotype association discovery
AI Agents
会调用工具、协同工作的智能体 Tool-using, collaborating agents
以 LLM 为大脑、工具为手脚：自动检索文献、调用分析管线、执行分子模拟，多智能体协作完成从假设生成到实验设计的复杂任务。
LLMs as brains, tools as hands: agents retrieve literature, run analysis pipelines and molecular simulations, collaborating from hypothesis generation to experiment design.
自动化文献综述与假设生成 Automated review & hypothesis generation
虚拟筛选与分子对接流水线 Virtual screening & docking pipelines
患者随访与健康管理 Agent Patient follow-up agents
03 · Full-Cycle Care 应用闭环
疾病全周期管理 Full-Cycle Disease Management
从风险尚未显形，到分子级别的干预 —— AI 介入疾病旅程的每个关键节点。滚动点亮流程，点击卡片展开细节。
From invisible risk to molecular-level intervention — AI assists at every key node of the disease journey. Scroll to light up each phase; click a card to expand.
PHASE 01 · EARLY WARNING
风险预警 Early Warning
在疾病发生之前看见风险 —— 融合可穿戴时序信号、体检指标与遗传易感性，构建个体化动态风险画像。
See risk before disease onset — fusing wearable signals, checkup metrics and genetic susceptibility into a dynamic personal risk profile.
AI 介入 · AI ROLE
时序模型对心电/血糖/睡眠信号进行异常前兆检测 Time-series models detect pre-anomaly patterns in ECG / glucose / sleep signals.
AI 介入 · AI ROLE
多基因风险评分 × 生活方式的个体化分层 Polygenic risk scores × lifestyle for individualized stratification.
PHASE 02 · DIAGNOSIS
智能诊断 Intelligent Diagnosis
多模态大模型联合阅片、读片、读病历 —— 为医生提供带证据链的第二诊疗意见。
Multimodal models read scans, slides and records together — offering second opinions with traceable evidence chains.
AI 介入 · AI ROLE
影像 + 病理 + 检验的多模态联合辅助诊断 Joint imaging + pathology + lab multimodal diagnosis.
AI 介入 · AI ROLE
可解释证据链：每个结论可溯源到影像区域与文献 Explainable evidence chains traceable to image regions and literature.
PHASE 03 · DRUG DISCOVERY
个性化药物研发 Personalized Drug Discovery
从靶点发现到分子生成 —— AI Agents 驱动的药物发现流水线，让"一人一策"的治疗成为可能。
From target discovery to molecule generation — agent-driven pipelines make "one patient, one strategy" possible.
AI 介入 · AI ROLE
生成式模型设计候选分子，虚拟筛选百万级化合物库 Generative models design candidates; virtual screening over million-scale libraries.
AI 介入 · AI ROLE
基于患者组学谱的药物重定位与联用推荐 Drug repurposing and combination recommendations from patient omics.
04 · Ethical AI Foundation 系统底座
Ethical AI 系统级底座 Ethical AI as the System Foundation
不是附录，而是地基。可信、隐私与公平写进系统架构的每一层。悬停（或点击）查看每个支柱的技术内涵。
Not an appendix, but the groundwork. Trust, privacy and fairness are constraints written into every layer of the architecture. Hover (or tap) to explore each pillar.
Trustworthy AI
可信 · 可解释 · 可验证
不确定性量化、可解释性归因、幻觉检测与临床安全围栏 —— 让每次输出都可被审视与复核。
Uncertainty quantification, explainable attribution, hallucination detection and clinical safety guardrails.
Private AI
隐私保护 · 数据不出院
联邦学习、差分隐私与同态加密：模型走向数据，而非数据流向模型，原始病历永不离开本地。
Federated learning, differential privacy and homomorphic encryption: models travel to data, raw records never leave the hospital.
Fair AI
公平 · 普惠 · 无偏
系统性偏倚审计与公平性约束训练，确保模型在不同年龄、性别、地域与种族人群中表现一致。
Systematic bias audits and fairness-constrained training keep performance consistent across age, gender, region and ethnicity.
以上三大支柱贯穿数据层、引擎层与应用层的每一个模块。The three pillars run through every module of the data, engine and application layers.
— FOUNDATION LAYER —Whether you are interested in collaboration, joining the lab, or just want to learn more about our work, we would love to hear from you.