Langhui Tech is deeply engaged in the healthcare domain, building 34 high-quality annotated datasets across four core areas — Medical Imaging, Pathomics, Medical NLP, and Knowledge Graphs — providing end-to-end data support from pre-training to fine-tuning for medical AI large models.
From AI-assisted diagnosis to AI drug discovery, from research breakthroughs to clinical deployment — medical AI is reshaping the future of health
Models like GPT-4V Medical, Med-PaLM 3, and HuatuoGPT-II achieve multimodal medical understanding, with clinical decision accuracy reaching expert-level performance. A 2025 Nature Medicine study shows that AI-assisted diagnosis now surpasses the average resident physician in accuracy across 12 specialties.
AlphaFold 3 enables joint prediction of proteins + RNA + small molecules, cutting drug R&D cycles by 50%. Insilico Medicine's AI-designed drug has entered Phase III clinical trials, marking the transition of AI drug discovery from an auxiliary tool to a core engine.
General medical imaging foundation models (RadFM, BiomedCLIP) cover all modalities — CT/MRI/X-ray/Ultrasound/Pathology — with a single model supporting diagnosis of over 100 diseases. The Lancet Digital Health shows that multimodal AI outperforms specialist physicians in rare disease detection.
RWD has become a core asset for pharmaceutical companies, driving paradigm shifts in drug R&D, post-market evaluation, and precision medicine. The FDA has approved 50+ new indication applications based on RWD, and NEJM emphasizes that high-quality RWD is a critical infrastructure for AI healthcare.
WSI (Whole Slide Image) analysis AI achieves precise classification of pathology subtypes, helping pathologists improve diagnostic consistency. Google Health's CONCH model published in Cell is the first to achieve cross-organ pathology universal representation, with zero-shot diagnostic accuracy reaching 92%.
Multi-source heterogeneous medical knowledge fusion supports large-model reasoning chains and explainable AI clinical decisions. The AAAI 2026 Best Paper proposes the MedReason framework, embedding knowledge graphs into medical LLM reasoning chains — improving explainability by 40% while maintaining diagnostic accuracy.
Building high-quality data infrastructure covering the entire healthcare chain, empowering AI+Healthcare innovation
Covers 10 major modalities including CT/MRI/X-ray/Ultrasound/Endoscopy/Pathology, with 20M+ training samples
100M+ professional medical NLP annotated records, covering 12 task categories with 66 sub-datasets
Fine-grained annotations of whole slide digital images, covering key diseases such as liver cancer, breast cancer, and mixed tumors
5M+ medical entities and 20M+ relation triples, mapped to ICD-10/11 and SNOMED CT
We have established compliant data collaboration mechanisms with 50+ Tier 3A hospitals nationwide, covering top medical institutions such as PUMCH, Xiangya Hospital, and West China Hospital, ensuring authoritative data sources, diverse samples, and professional annotations.
A three-tier quality control system of licensed physician annotation, chief physician review, and AI inspection ensures the accuracy and consistency of every data annotation, with overall annotation accuracy exceeding 99.8%.
Strictly compliant with HIPAA, GDPR, and China's Personal Information Protection Law and Healthcare Data Security Guidelines. All datasets undergo de-identification, with complete authorization chains and support for compliance audits.
Our proprietary intelligent annotation platform supports automatic pre-annotation of medical imaging, NLP intelligent extraction assistance, and AI segmentation of pathology images. Human-AI collaboration boosts annotation efficiency by 3-5x while maintaining expert-level quality control.
Categorized by domain, covering four core areas: Medical Imaging, Pathomics, NLP, and Knowledge Graphs
5 million chest X-ray DICOM images with structured diagnostic reports, double-reviewed gold standard
5 million head CT plain + contrast DICOM series with diagnostic reports
5 million upper abdominal multi-organ cancer annotations, covering liver/gallbladder/pancreas/spleen/adrenal/kidney
High-quality color fundus photographs with structured diagnostic reports, based on ICDR international standards
Includes dermoscopy images, clinical photos of skin lesions, and pathology gold-standard diagnostic reports
5 million endoscopy videos + key frame screenshots + diagnostic reports, including polyp location/size/classification
20M+ medical imaging training samples, full coverage of CT/MRI/X-ray/Ultrasound/Endoscopy/Pathology
General annotated medical imaging dataset covering four modalities: CT, X-ray, Ultrasound, and MRI
1M+ annotated ultrasound imaging frames, multi-device multi-site coverage
1M+ DICOM image slices, covering 7 major organs, thin-slice CT 512×512+
52.8K cases of hepatocellular carcinoma multi-sequence MRI DICOM images with professional physician annotations
52.8K DICOM images with professional physician annotations, covering multiple skin diseases
5 million 12-lead ECG waveform images with diagnostic reports, including auto-analysis and physician review
52.8K DICOM images with professional physician annotations, covering fracture detection to surgical planning
1M+ tongue/face images and 50,000+ professional annotations
1M+ annotated surgical video frames, pixel-level semantic segmentation of 24 anatomical structures and surgical instruments
1M+ OB/GYN clinical datasets, covering prenatal emergencies, high-risk pregnancies, labor and postpartum — full pregnancy cycle scenarios
100M+ professional medical NLP annotated records, covering 12 task types including entity extraction, dialogue understanding, and chain-of-thought
50 million de-identified EMRs, covering chief complaint, present illness history, past medical history, admission records, and discharge summaries
328K NER and relation extraction annotations from EMRs, diagnostic reports, physician orders, and literature abstracts
Massive authoritative medical knowledge covering medical textbooks, clinical guidelines, review articles, drug instructions, and more
20 million inpatient full-cycle text/PDF documents, covering medical record QC/DRG/DIP
70K medical journal articles, including illustrated medical literature and clinical case reports
5 million MDT consultation texts for difficult cases, supporting AI training for rare and complex diseases
1M longitudinal tracking structured data for chronic diseases, with 7+ consecutive inpatient follow-ups
5 million real structured physical examination data, gender-balanced, multi-age-group stratified
1M+ in-depth physical exam reports, 800+ structured medical fields
Whole slide digital images (20x+ scan magnification) with complete pathology diagnostic reports, including immunohistochemistry results
5 million full-process pathology diagnostic images + texts, covering the complete diagnostic chain
5 million QuPath fine-grained regional annotations, HCC/ICC/MID/TLS/MVI six-class labels
1 million liver lesion image annotations, differential diagnosis of liver cancer and hemangioma
1 million breast tumor image annotations, supporting AI screening and diagnosis of breast cancer
5M+ medical entities and 20M+ relation triples, mapped to ICD-10/11 and SNOMED CT
3M+ specialty disease structured data, covering 26 key disease cohorts including diabetes, stroke, and tumors
View annotation structures and sample data across different types of medical datasets
Core application scenarios powered by Langhui healthcare datasets
AI models trained on medical imaging datasets assist physicians in rapidly and accurately diagnosing 100+ diseases
Supports drug molecule design, target prediction, and clinical trial optimization to accelerate new drug R&D
Medical large language model-powered intelligent consultation systems enhance primary care service capabilities
AI health assessment and risk warning systems based on physical exam and chronic disease data
Providing real case data for medical education AI, enhancing clinical reasoning skills of medical students
Building medical knowledge graphs to support explainable AI clinical decision-making and knowledge Q&A
Frequently asked questions about Langhui healthcare datasets
Langhui Tech strictly complies with HIPAA, GDPR, and China's Personal Information Protection Law and Healthcare Data Security Guidelines. All datasets undergo de-identification, removing patient names, ID numbers, contact information, and other identifiable data to ensure compliant data use. We have also established compliant cooperation mechanisms with Tier 3A hospitals to obtain data usage authorization.
Langhui adopts a three-tier quality control system of "physician annotation + expert review + AI inspection." Medical imaging is annotated by licensed radiologists and pathologists, with annotation results reviewed and confirmed by chief physician-level experts. AI-assisted inspection tools are introduced to detect annotation consistency, with overall annotation accuracy exceeding 99.8%.
Multiple standard medical data formats are supported: DICOM (medical imaging), WSI (whole slide pathology), HL7/FHIR (clinical data), JSON (structured annotations), PDF (medical documents), and more. We also provide unified data interfaces and SDKs for direct loading by AI models.
Yes. Langhui Tech offers customized data services, collecting and annotating datasets for specific diseases according to customer needs. We have a data source network covering multiple Tier 3A hospitals nationwide and can respond quickly to customization requests. The cycle from data acquisition to annotation delivery typically takes 4-8 weeks.
Please contact our sales team via the website contact form or by calling the business hotline at 137-5502-0164. We will provide corresponding data authorization solutions based on your use case (research/commercial/education) and sign a data usage agreement.
Langhui Tech healthcare datasets provide high-quality, compliant, and trustworthy data support for AI+Healthcare innovation