Dataset Overview
Definition：Licensed Medical Journal Datasetis provided byChangsha Langhui Information Technology Co., Ltd.A collection of high-quality medical journals obtained through authorized compliance transfer channels.
| Dataset Name | Authorized Medical Journal Graphic Datasets |
| Total Data Volume | 70,000 Medical Journal Articles |
| Data Type | Images (medical images/charts) + text (full text) |
| Data Source | Resale (authorized by the publisher of the medical journal) |
| License Type | Sublicensing Complete authorization documents are attached |
| Delivery Cycle | 1–2 months |
| Section Coverage | All specialties: internal medicine, surgery, obstetrics and gynecology, pediatrics, orthopedics and psychology, neurology, oncology, ophthalmology |
| Content Type | Clinical case reports, clinical guidelines, medical reviews, image interpretation, surgical diagrams, drug descriptions |
| Language | Primarily Chinese, with partial bilingual English comparison |
Core Data Fields
Each article contains complete structured metadata and textual content, covering the full range of needs from literature retrieval to deep learning training.
| Field Category | Field Count | Typical Fields |
|---|---|---|
| Bibliographic Metadata | 12 | Article title, journal name, year of publication, volume number, author, author affiliation, DOI, keywords |
| Section Classification | 8 | Level I, II, ICD code, article type (original research/review/case report) |
| Text | 6 | Abstract, introduction, methods, results, discussion, conclusions (full paragraph text) |
| Picture Data | 10 | Images, pathological slides, surgical diagrams, cropped sections of examination reports, charts, image titles, image descriptions |
| References | 5 | Quoted number, cited title, cited source, cited year, cited DOI |
| Authorization Information | 4 | Copyright holder, type of authorization, scope of authorization, duration of authorization |
Data Sample Showcase
The following is a model of a structured, de-identified JSON model showing typical data formats for medical journal articles.
Other Organiser
"article id": "MED-J-2023-004512,
"Meta":
"title": "Deep learning-based study on clinical application of chest X-ray pulmonary arthropod test"
"journal": "China Radiology Journal",
"year": 2023,
"volume": 57,
"issue": 8,
"doi": "10.3760/cma.j.cn1121449-20232155-00123",
"Authors": ["Zhang O-Tai", "Lee O-Tai", "Wing O-Tai",]
"affiliations": ["The radiology department of the first hospital attached to a medical university,"
"Keywords": ["Pulmonary Constance Festival", "Deep Learning", "Mits X-ray", "Computer-assisted Testing"]
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"classification":
"department l1": "Image Section,"
"department l2": "bentography,"
"disease code": "R91.1",
"article type":
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"content":
"abstract": "Purpose: to assess the value of the clinical application of computer-aided detection systems based on in-depth learning in chest X-ray pulmonary arthropod tests. Methodology: Retrospective collection of 5,280 cases of chest X-rays in the hospital between January 2021 and December 2022...
"Methods": "Train pulmonary knot detection models using the ResNet-50 framework, training 3,000 cases, validation 1,000 cases and testing 1,280 cases. The gold standard is based on the results of a double blind reading by two senior radiologists." I don't know.
"Results": "AUC reached 0.92 on the test set with 87.3% sensitivity and 91.5% specificity. For the end of >5mm diameter, the detection rate is 94.2%. " ,
"Conclusion": "Deep learning-based CAD systems have high clinical application value in chest X-ray pulmonary arthropod tests, which can be used as auxiliary tools for radiologists."
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"images"...
Other Organiser
"img id": "FIG-001",
"type": "X-ray image",
"caption": "Figure 1 8 mm in diameter of the upper right lung, as shown by the arrows"
"Format": "DICOM"
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"license":
"Copyright holder": "China Medical Society",
"License type": "Transfer-Commercial AI Training,"
"valid until": "2028-12-31"
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♪ I'm sorry ♪AI Application Scenarios
Building the medical knowledge base
Seventy thousand cases of structured medical literature have injected authoritative knowledge into the large model of medical care, supporting the construction and updating of the medical knowledge graph and the knowledge base for clinical decision-making.
Medical model SFT training
High-quality image-text matching data are used to fine-tune medical-domain supervised fine-tuning (SFT) for base models such as GPT/Llama/Qwen to enhance medical specialization.
Medical multimodal model training
Images are matched with text reports, supporting training of vision-language multimodal medical AI models, as well as automated reading and report generation.
Clinical decision support (CDSS)
Training of CDSS models based on clinical guidelines and case reports, providing clinical practitioners with complementary diagnostic and treatment recommendations grounded in evidence-based medicine.
Medical literature retrieval and analysis
Structured metadata support the use of medical informatics applications such as intelligent literature search, automatically generated digests, research trend analysis, etc.
Medical education AI
A wealth of case and video materials can be used for training in the AI-based medical education system, supporting the development of clinical thinking and case analysis skills for medical students.
Frequently asked questions
Domain expert network
Covering six major disciplines: mathematics, physics, chemistry, biology, earth sciences, and computer science
Academic credential review, professional competency assessment, data annotation training, and continuous quality monitoring
AI pre-annotation + expert review, 300% efficiency improvement, 99.5% accuracy
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