I. Expert guide: Cholesterol disease - digestion is the "last fortress".
I've been dealing with the most complex anatomy area in the body for 35 years — the gallway and pancreas. This area, known by surgeons as the "removal last fort", is well structured, with disease displays so strangely mist, and even the most experienced goggles are often "skinned" under the choreograph.
Cystic cancer, pancreatic cancer, known as "the king of cancer," is extremely difficult to diagnose at an early stage, 80% of patients are diagnosed at an advanced stage and have a survival rate of less than 10% for five years. Cystic lenses, the "supereye" that directly observe the gallows and pancreas mucous membranes, are the most powerful weapon for detecting cholesterol tumours at an early stage. However, the learning curve of this technique is extremely steep, with a capacity to operate cholesterol lenses and to identify the disease accurately, with a capacity of lens specialists, who are few than hundreds of people nationwide.
In recent years, artificial intelligence has given new hope to solve this dilemma. But I must say frankly: the AID diagnosis of cinnamon is the most difficult "hard bone" in the whole digestive inner-scope AI field. Where is the problem? The first thing that comes to mind is data -- high quality pancreas image data are extremely scarce.
In this article, I will look at clinical pain spots in this area from the perspective of a cholesterol surgeon, the breakthrough in frontier technology in 2026, and the strategic significance of high-quality data sets for the development of cholesterol disease. I believe that understanding the complexity of cholesterol disease is a way to truly understand the value of a good data set.
II. Trade pain: The "Quadretex" of diagnosis of cholesterol disease
2.1 "Old Disorder" - The King of Cancer's Hiding
Early signs of cholesterol and pancreatic cancer are extremely hidden, and patients are often free of symptoms of specific diseases, which have advanced to the middle and late stages when symptoms of yellow sting, abdominal pain, and loss of body weight have occurred. In the case of cholesterol cancer, the survival rate for early cholesterol cancer (Tis/T1) can be over 80%, while the period of progress (T3/T4) has dropped sharply to less than 20%.
Insulin lenses can directly observe mucous changes in galleries and pancreas, and are theoretically the best means of detecting tumours early on. But the reality is that the incubation lenses are difficult and risky, performed only at a few large health centres, and most patients do not have the opportunity to undergo this examination early. The difficulty of finding the late stage is the greatest clinical pain in the area of cinclinic disease.
2.2 "Learning hard" - the Everest level of endoscope technology
If the internal lens is the "crowd" of medicine, the cinnamon lens is the "ming bead" on the crown – the most difficult to master. It is usually five to seven years for a digestive internal lens doctor to master conventional gastrointestinal lenses, on the basis of which an additional three to five years of specialized training is required to operate the ERCP independently (the chording of the increative incendiary tube), on the basis of which the caincinary lenses are performed, and two to three years of study.
According to the Insights Chapter of the China Medical Association, fewer than 500 doctors are able to perform an independent check-up of cholesterols, mainly in the first-line cities. This extremely uneven distribution of health resources prevents patients in large-scale municipal hospitals from accessing quality cinnamon treatment services.
2.3 "Qualitative Difficulty" - Complexity of diagnostic identification under mirror
Under the pancreas, inflammation, rock, tumors are often "me and me." Choledoitis can be manifested in mucous blood, oedema, curvature, and high levels of subclimate, similar to early cholesterol cancer; long-term cocinary stimuli can lead to mucular growth, chemical growth, or even non-typical growth, and incognito diseases that are difficult to identify.
A multi-centre study covering 15 Sanctuary hospitals across the country shows that even experienced insulin-species specialists can determine the accuracy of a cholesterol-based malignant pathologies by using a mirror alone at about 75-80%. This means that one in four to five patients is likely to be misdiagnosed.
2.4 "Data hard" - data from rare diseases + high threshold
The pancreatic lens data are called "Oasis in the Desert" compared to the number of gastrointestinal cosmoscopes in the million-degree range. The reasons for this are three:
Reason one: Low number of inspections.The annual checkup of cholesterol is estimated at less than 200,000 cases, which is a difference from the hundreds of millions of cases of gastrointestinal lenses.
Reason two: difficulty in marking.The identification and labelling of pathologies under the cholesterol lens requires high-level insulin lenses, which are very scarce in themselves. At the same time, the pathological criteria are difficult to obtain – the operation of choreographies is difficult, the positive rate is low, and many pathological conditions are not readily diagnosed.
Reason three: The proportion of rare diseases is high.Malignant diseases such as cholesterol cancer and pancreatic cancer are relatively rare, with a limited number of single-centre annual diagnoses, and it takes many years to accumulate enough positive samples. That is why open cholesterol data sets usually account for thousands of cases.
Data scarcity directly constrains the pace of development of cholesterol disease. I have seen too many teams, algorithms are well designed, but because of insufficient training data, models are never able to break the threshold of clinical application. This is the central reason why Longway’s ENCO-CHOLANGIO data sets are valuable – they have created a standard gold data set of 100,000-scale sizes in one of the most data-poor areas.
III. 2026 Frontline breakthrough: five technological innovations for Cough Pancreas AI
Despite the challenges of data scarcity, the first-ever breakthrough in the area of cholesterol disease in 2025-2026 has been remarkable. The world’s leading team, the Medical Aid team, is "countering" data scarcity through technological innovation.
3.1 Less sample learning for rare diseases: breaking the Achilles trail of scarce data
The Small Sample Learning (Few-Shot Learning) is one of the core technical directions of AI for cholesterol disease. In late 2025, the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) jointly released the ProtoCholangio model, using Prototypic Network, and using the meta-Learning strategy, high-precision recognition of the bad pathological pathology of the cholesterol was achieved with only dozens of rare cholesterol cancer samples.
The specific data are encouraging: the ProtoCholangio model, set at Few-Shot, which uses only 50 training samples of cholesterol cancer, has a UC of 0.892, a sensitivity of 82.3%, an specificity of 80.1%, and a traditional method of supervised learning of 0.715% with the same data, with a difference of 17.7 percentage points. The study opens a new technical path to the diagnosis of rare diseases in AI.
In 2026, this direction continued. The team at the Riggin Hospital, affiliated with the Shanghai University of Transport Medical School, based on the Longway ENCO-Cholangio data set, proposed a new model of a small sample learning framework, Few-Cholangio, which, combined with a comparative learning and a monetary learning strategy, reached 0.876 points in the rare pancreas cancer detection mission, in 5-shot, which represents an increase of 15.2 percentage points over the baseline methodology.
3.2 Multimodular Integration Diagnosis: ERCP+EUS+Caucous Pancreas Mirror "Triples"
Clinical diagnosis of cholesterol is never a single examination, but a combination of ECP (inner mirrors against cholesterol), EUS (ultrasonic endoscopy), and pancreatic lenses. Multimodular integration was an important research guide for cholesterol disease AI in 2026.
The MultiMod-Bilitary model, published by the Johns Hopkins University School of Medicine, for the first time, integrates in depth three mosaics of ERCP images, EUS ultrasound images, and cholesterol lens images. The model uses a cross-modular Transformer structure to automatically learn about the linkages and complementarities of different mosaics through attention mechanisms. In the case of the cholesterol cancer diagnostic mission, the AUC of the MIM model reaches 0.943, an increase of 6.7 percentage points compared to the single chord lens (AUC 0.876).
More encouragingly, multi-modular integration is particularly valuable for early diagnosis of cholesterol cancer – the sensitivity of detection of choreography 1 has increased from 68% to 84% of the single modulation. This means that MMA AI is expected to truly break the “early diagnosis difficulties” of cholesterol tumors.
3.3 Tumour immersion depth prediction: the "qualitative" to "phased" leaps
For cholesterol, not only is it cancer, but it is the T-phase of the tumor that determines the procedure and the prognosis. In 2026, the dysentery of the tumor is predicted to be a new hot spot for cholesterol.
The Cholangio Depth model, published by the Tokyo University Medical Department in conjunction with the National Cancer Research Centre of Japan, has achieved predictions of the depth of immersion in cholesterol cancer by means of time-series characterization of cinnamon lens videos, combined with in-depth study of fine capture of mucous film surface patterns. The total accuracy of the model for the T1/T2/T3 period was 72.5%, of which Tis/T1 and T2+ were 84.2%. This level is close to the judgement of the highly skilled inoculation specialists.
The clinical significance of this technology is that if AI is able to accurately assess the tumour infestation depth before the surgery, it will help to choose a more appropriate treatment - under-implant treatment can be considered for the early pathologies of Tis/T1 and major surgery can be avoided; in the case of T2+, surgical tycotomy is required. This precision period can significantly improve the quality of life of the patient.
3.4 Articular real-time navigation: from "Looking at the speech" to "Operational guidance"
The value of AI is not just diagnosis, but also guidance. In 2025-2026, AI navigation technology made a major breakthrough in cholesterol mirrors. The Cholangio Nav system, published by the University of Technology in Munich, Germany, enables real-time navigation support in the course of ERCP and cholesterol operations, including automatic identification of choreography structures, real-time alerting of the location of the stove, referral of the biopsy, early warning of operating risks, etc.
In a random control test that included 200 patients, the success rate of the first intubation was increased from 78 to 89 per cent, from an average of 38 minutes to 29 minutes, and from 62 to 75 per cent of the active positive rate. This means that AI navigation not only improves operational efficiency but also the accuracy of diagnosis.
Of even greater concern is the fact that the ANS is particularly significant for low-age physicians - after using ANS, low-age doctors (operatorship)<200例）的首次插管成功率从61%提升至85%，接近高年资医师的水平。这为胆胰内镜技术的基层推广提供了有力的技术支撑。
3.5 Trans-equipment: addressing the data drift challenge
The large number of brands of cholesterol equipment (Olympus, Fuji, Bind, etc.) and the wide variation in the quality of image, colour reduction, resolution of different equipment, have led to the poor generalization of the AI model between different devices — the “last kilometre” challenge of medical AI landing.
In 2026, important progress was made in addressing this problem. The DomainCholangio framework, proposed by the Stanford University team, has been generalized across equipment and centres using a combination of technical routes for adaptation and style migration. Experimental data shows that, when the model for training Olympus equipment was moved to Fuji, the AUC of the traditional method was reduced from 0.91 to 0.73, while the DomainCholangio framework maintained the AUC after migration at 0.88, with a reduction in performance ranging from 18 to 3 percentage points.
2026 A Frontline Technical Indicator Summary of Cholesterol Diseases
| Technology Direction | Representative Models | Core performance indicators | Publication institution/periodics |
|---|---|---|---|
| 少样本学习 | ProtoCholangio | 50-shot下AUC 0.892 | MIT CSAIL / Nature Biomedical Engineering |
| Multimodal Fusion | MultiMod-Biliary | Diagnosis of choreography AUC 0.943 | Johns Hopkins University |
| Bleeding depth prediction | CholangioDepth | T-phase accuracy rate 72.5 per cent | University of Tokyo / Gut |
| 术中导航 | CholangioNav | 89% success rate for first intubation | Munich Industrial University |
| 跨设备泛化 | DomainCholangio | Cross-equipment AUC 0.88 | 斯坦福大学 |
IV. ENDO-CHOLANGIO DIVISION DEVIEW: KEY FOR THE DELIBERATION OF CLASS INNAI DATA
After detailing the front-line technologies, I must stress once again the fundamental fact that all these algorithmic innovations, whether they be small sample learning, multimodular integration, or cross-equipment, are ultimately dependent on data support. And the ENO-CHOLANGIO video-image data set, built by Changsalang, is the largest, most systematic, and most controlled cholesterol data set in the country and in the Asia-Pacific region.
ENDO-CHOLANGIO CORE ALTERNATIVES FOR THE DATA DESERTIFICATION
4.1 Data size: the leap from "thousands" to "100,000"
100,000 cases of cinnamon mirror video images – let me use a few references to help you understand the magnitude of this figure. The most daring pancreatic lens data set available internationally is the Cholangio-DB of the National Cancer Research Center of Japan, about 8,000 cases; the country’s open data set is generally in the range of 2,000-5,000.
For the "data scarcity" area of cholesterol disease, what does a 100,000-scale data set mean? It means that the AI model no longer needs to struggle on small data sets, but can learn the diversity of the pathologies based on sufficient data. This is decisive for improving modelability and reducing the risk of over-adaptation.
I understand that Longhui Tech worked with data from more than 20 Sana hospitals across the country to build up this data set over five years ago. This "slow work" patience and determination is particularly rare today in a business environment that seeks "quick realization."
4.2 Double-diverse coverage: complete layout of the gallway + pancreas
Another significant advantage of the ENDO-CHOLANGIO data set is that it covers both the cholesterol and pancreatic tracts. This is important because, in clinical practice, cholesterol diseases and pancreas diseases are often interrelated and interact with each other — gallstones can induce pancreasitis, and pancreas tumours can oppress the chords and cause obstructive yellows.
The vast majority of the current cholesterol-related data set on the market covers only a single part (either a gallow or a pancreatic) and leads to the AI model being able to handle only a single part of the disease, and not to cope with complications such as a disease at the cholesterol intersection. The dual coverage of the ENCO-CHOLANGIO provides the AI model based on the data set with a more comprehensive diagnosis of cholesterol, which can handle a full range of disease from the larvae, the choreal tube, the kettlet and the pancreas.
4.3 Pathological gold standard: "The test stone" for choreographing insulin.
In the area of cholesterol, the pathological criteria are much more difficult to obtain than in other digestive organs. Because of the depth of the cholesterol anatomics, the thinness of the tube, the abundance of the surrounding veins, the difficulty of performing cholesterol biopsies, the high risk of complications, and the inability to identify pathological conditions.
The most striking feature of the ENDO-CHOLANGIO data set is that all the disease stoves have tissue pathology results as a gold standard. Behind this is a strict "Imogen-pathology matching process" set by Longhui Technologies, which in every cholesterol examination, the biopsy specimen is precisely marked to the corresponding point, and the pathological diagnosis is well related to the under-sight.
The value of the gold standard cannot be overemphasized.In the medical field of AI, there is a iron law called Garbage In, Garbage Out. If the training data label is not accurate per se, then the AMA model is only "airhouse." Especially in the field of cinnamon disease, where it is difficult to identify itself, there is no AIS model for pathological gold standards, which is probably just "learning wrong."
The ultimate desire for data quality, which is upheld by ENDO-CHOLANGIO, is, in my view, the most competitive of the data set and the very indicator of its difference from the same product.
4.4 Total coverage: System classification of three major categories of rock/tumour/inflammation
The ENDO-CHOLANGIO data set covers the three most common types of disease under cholesterol lenses - stones, tumours, inflammations - and sub-categories under each of these categories:
结石类：These include cholesterol, cholesterol, mixed, pancreatic, etc., and are characterized by the size, quantity, location, and presence of the stones.
肿瘤类：Among them are cholesterol cancer (cuphol, nostrils, immersion), pancreas, adenomas, neuroendocrine tumours, etc., indicating the broad type of tumor, growth pattern, surface characteristics, vascular texture etc.
炎症类：These include bacterial choreitis, autoimmunochocalitis, chronic pancreas, insulinitis, etc., indicating the extent, extent, mucous changes, etc.
This whole-of-path, systematic system of classification of diseases allows data sets to support not only the "good and bad identification" of the two categories, but also the more sophisticated disease stratification. For the AI product, this means that the function is more extensive -- not only to tell the doctor if there is a problem, but also to tell the doctor what the problem is.
4.5 Eight-dimensional structured label: from "identify" to "understand"
The marking system of ENDO-CHOLANGIO in Longway has reached eight dimensions, which is a rare degree of precision in the concentration of cholesterol data:
First dimension: classification of diseases- Three broad categories of rock/tumour/inflammation and detailed sub-categories.
Second dimension: disease-based mapping- Precise signs of the location of the dissection of the stove (fatal choreline/expain chord/breath/insulin pancreas/insulins etc.).
Third dimension: disease-based morphology- morphological description of the size, shape, boundary, surface characteristics of the stove.
Fourth dimension: membrane characterization- Fine features of the mucous membrane, texture, blood vessels distribution, hemorrhaging, etc.
5D: Description of cavity- The narrowness of the cavity, the extent of expansion, the change of form, etc.
6D: Operational label- Marks for biopsy, treatment (stone extraction, stubling, etc.).
7D: Pathological links• The pathological diagnosis of each of the stoves, with a precise image-pathology response.
8D: Clinical information links• The association of clinical information on the age, sex, symptoms, laboratory tests (oncology markers such as CA19-9, CEA), other visual examinations (CT, MRI, EUS).
This eight-dimensional system of labelling provides the AI model with an extremely rich monitoring signal. The AI system that is trained on this data is no longer a simple image sorter, but rather a diagnostic aid system with a certain "clinical thinking" component.
4.6 Quality control system: four-track level-protected data lifeline
For medical data sets, quality control is "lifeline." Longhui Technologies has set up a strict four-level quality control system:
Level 1: Data acquisition quality control.Develop harmonized incubation mirrors and image collection specifications, including equipment model parameters, operating processes, image quality standards, etc., to ensure consistency and standardization of data.
Level 2: Marked personnel quality control.All the labeled personnel are systematically trained and certified, with a pass rate of less than 30 per cent ensuring the professional level of the team.
Level 3: Double blindmark + expert arbitration.Each case is independently marked by two certified goggles, and the inconsistent cases are referred to the Arbitration Commission, composed of experts from the Deputy Medical Officer and above.
Level 4: Regular quality audits.Quarterly random sampling quality audits of data sets are conducted, with external experts assessing the quality of the markers to ensure that the overall quality of data sets remains high.
V. CLIMINICAL APPLICATION: THE FIVE DATES OF DATA ENFORCEMENT
With high-quality data sets like ENDO-CHOLANGIO as the base, AI technology can really take root in clinical scenes of cholesterol disease. Here, I'm going to paint the most valuable clinical applications from five dimensions.
Scenario one: Auxiliary diagnosis of cholesterol disease - improvement of diagnosis accuracy
This is the most basic and important application scenario. The AIS diagnostic system, based on ENDO-CHOLANGIO training, allows real-time identification of the stoves, alerts to the risk of malignity, and advice on disease characterization during cholesterol examination. For the endoscopy doctors at the local hospital, this is equivalent to a "top expert" who guides them in real-time at the time of operation.
Scene 2: Early screening for cholesterol cancer -- breaking the "discovery" and "late" dilemma.
For groups at high risk of chord cancer, such as conch stones, pre-scrutinitis, and congenital cholesterol expansion, regular screening of cholesterol is key to early detection of chord cancer. The screening coverage of high-risk groups is extremely low, owing to limited resources of cholesterol specialists.
Scene 3: Insulin screening -- Unmasking the "mysterious veil" of pancreas
The diagnosis of pancreas is traditionally a clinical difficulty - the pancreas are deep and routine examinations make it difficult to detect early pancreatic changes. Concreas lenses directly observe incline mucous membranes, but they are difficult to operate and difficult to identify.
Scenario 4: ErCP-assisted navigation - lower operational threshold
ERCP is a core technique for cholesterol treatment, but it is difficult to operate and is a long learning curve. The AI-Auxiliary Navigation System provides real-time guidance in the course of the ERCP operation: automatic identification of nipple positions, hinting of intubation, pre-judgement of cholesterol insulin walk, warning of perforation risks, etc. This is of great value in reducing the learning curve and helping junior physicians to quickly master the ERCP technology. The navigation model based on ENDO-CHOLANGIO training in the ENDO-CHO data set allows for adaptation to different anatomical and pathological variations, making AI navigation more accurate and reliable.
Scene 5: Active screening guidance - improving the positiveness of the screening
The positive rate of the sub-clinic examination is influenced by the operator’s experience – less experienced doctors may not be able to get a true pathological tissue, leading to false negative results. The AI-directed active screening system automatically identifies the most suspicious diseased areas, and is precise in guiding the extraction of the disease to improve the positive rate.
VI. Social benefits and industry values: from "data assets" to "life values"
6.1. Increase in the rate of early diagnosis: from "The King of Cancer" to "Cutable Disease"
If you find these tumors at an early stage, the patient's prognosis will be a qualitative leap. In the case of choreography, the survival rate for patients in T1 is over 80% over five years, while T4 is less than 5% – a 16-fold difference.
The widespread screening of AI-assisted cholesterol lenses is expected to increase the early detection rate of cholesterol tumours from less than 20% to more than 40%. Every additional case of cholesterol tumours may save one life and save another family. In this sense, high-quality cholesterol data sets are not just technical assets, but also "life assets".
6.2 Grassroots empowerment: making high-level technologies more accessible to patients
The generalization of the AIS system will effectively break this pattern - via real-time AAI support, and endoscope doctors at the local hospitals can make an analysis of the situation at the level of specialists at the 3A hospital.
This will not only reduce the hardship and financial burden of patients’ access across provinces, but also facilitate the implementation of a hierarchy of treatments, and achieve the goal of "undisturbed" health care. The AI system, supported by the ENCO-CHOLANGIO data set, is becoming a digital bridge to the decline of quality health resources.
6.3 Efficiency gains: value of releasing expert resources
The AIS system can take on the tasks of primary screening, splitting, routine diagnosis, and so forth, freeing the specialists from repetitive work, focusing on the treatment of difficult cases and the operation of complex operations.
It is estimated that AI support can increase the efficiency of experts two to three times. This means that the same number of experts can serve patients three times more than they are.
6.4 Industrial value: building an innovative ecological system for cholesterol insulin
From an industry perspective, the ENDO-CHOLANGIO data sets fill the gaps in the country’s high-quality data sets for cholesterol lenses, providing a critical “infrastructure” for the development of the whole cholesterol industry. Through various models, including data authorization, joint R & D, scientific cooperation, more AI firms, medical equipment manufacturers, and scientific institutions can innovate on the basis of this data base, accelerating the transformation and landing of technological results.
I understand that there are now many top M.A. hospitals and AI companies in China that are working together on the basis of the ENDO-CHOLANGIO data sets of Longway, covering a number of directions, including A.A.A.A.A., a multi-party innovation. This ecological model of "data bottom plus" is the only way to achieve high-quality development in China’s medical AI industry.
VII. Expert vision: trends in cholesterol in the next 3-5 years
As a clinical practitioner with a long-term focus on the development of cholesterol disease, I have five judgements on the trends in this area over the next three to five years.
7.1 Trends I: From "Single-Ai" to "Multiscope-Ai"
The current insulin AI is based on a single endoscope (e.g. cholesterol lens) and the future direction is to integrate information on various endoscope techniques, such as multi-scopy combinations — cholesterol lenses, ultra-sonic endoscopys, tri-cogular laser micro-inspectives (CLE). Multi-synthetic AI can provide full-dimensional diagnostic information from cavity mucus to tube wall levels and then out-of-wall violations, and can actually achieve "one-stop" precision. This requires data sets to integrate multiple endotypes, and ENDO-CHOLANGIO is already working in this direction.
7.2 Trends II: From "diagnosis AI" to "diagnosis AI"
The future cholesterol AI will not be limited to diagnosis, but will be developed to "diagnosis integration" – not only to identify the pathology, the nature of diagnosis, but also to recommend treatment, guide treatment, and evaluate its effectiveness. For example, AI can not only tell doctors that there is a cholesterol here, but also to recommend the best way to get stone, to direct it in real time, and to assess whether it is clean. This integrated AI system of treatment will become a real "smart assistant" to an inner mirror.
7.3 Trends III: Few samples and self-monitoring learning become core technologies
Because of the relative rareness of cinclinic diseases, data scarcity will be a long-term challenge. Thus, data-efficient techniques like small sample learning, self-supervised learning, and weak monitoring learning will play an increasingly important role in the field of cinclinics.
7.4 Trends IV: AI access clinical guidance and health insurance payments
As AI assistive diagnostics mature and clinical evidence accumulates, I expect that in the next three to five years, the AAI aid diagnosis of cinnamon will be included in the relevant clinical guidelines as a recommended treatment. At the same time, the cost of AAI aid diagnostics is expected to be included in health insurance payments, thus further promoting the spread of AI technology.
7.5 Trends V: Value of medical data activated in the data factor market
As the market-based allocation of national data elements reforms advance, medical data will be recognized and released more fully as an important public health resource and strategic resource. High-quality, compliant medical data sets will become the core assets and competitiveness of medical AI companies.
VIII. CONCLUSION: THE DIGITAL LIGHT OF CLASSIN INCRUEL DISAPPEARANCES WITH THE LIGHT OF STATISTICS
I've seen too many cases of cholesterol disease in 35 years – many of them not because they have no medicine, but because they are found too late. I feel deeply sorry for every time I meet a patient who could have been detected early.
The invention of cholesterol mirrors gives us a pair of "eyes" that directly look at galleries and pancreas, but the value of these eyes can only be fully realized by being in the hands of experienced experts. And the emergence of AI technology shows the possibility of "replicating" expert experience – the digitalization and universalization of diagnostic capabilities of top-level experts through high-quality data and advanced algorithms, benefiting more patients.
I am convinced that in the near future, AAA-assisted pancreas will become clinical routine, that early detection of cholesterol and pancreatic cancer will increase dramatically, and that the crown of the King of Cancer will eventually be removed. On this difficult path, high-quality medical data sets will always be the most reliable weapon and the strongest backstop.
Let's use data as a basis, and use AI as a target, to attack this medical bastion of cholesterol disease.
About Changsha Langhui Information Technology Co., Ltd.
Chang Shalom Information Technology, a leading medical image AI data set provider and provider of smart diagnostic solutions in the country, focuses on the construction of high-quality medical image data sets in the fields of endoscopes, skin mirrors, and AI algorithms. The company’s core team, composed of senior medical experts, data scientists, and AI engineers, has built a multidisciplinary medical image data set matrix covering digestives, gynaecology, dermatology, etc., with a total of over 1.5 million cases, serving dozens of hospitals, scientific institutions, and AI companies in the country.