1.8 million cases of fractured quadrants: "Gold standard" for emergency bone service AI

Expert introduction: fractures — the most common "invisible trap" in the emergency room

As a veteran who has worked in emergency surgery for more than 30 years, I have been working at night, counting the number of broken bones and the 100,000 cases I have dealt with. But I must be honest, even if I see a trauma in the queue every time I see a patient on night shift today, I remind myself and the young doctor that I have been doing a 12-point-symbolt-break bug, probably in the next film.

The most common damage to the body of the emergency bone is the fracture of the limbs, which account for more than 70 per cent of all cases of the emergency bone. Every year, more than 30 million people suffer from the fracture of the limbs due to various kinds of trauma.

The consequences of the failure may be severe – from fracture to deformation, to functional impairment, to need for secondary surgery, and even to cause medical problems.

There are many reasons why fractures are so easily missed. There are many reasons: the fracture line is thinly hidden, the autopsy structure is complex, the patient is not well-equipped, the image is poor, the doctor is tired, etc. But the most fundamental reason is the diversity of fracture forms and the high empirical dependence on diagnosis. A radiologist or osteopathic doctor who can accurately identify complex fractures requires at least 5-10 years of professional training.

Today, 2026, artificial intelligence is revolutionizing the diagnosis of fracture images. From fracture detection to spectrometric recognition, from emergency screening to surgical planning, AI is increasing the efficiency and accuracy of fracture treatment in all its aspects.

Today, I would like to explore with you, from the perspective of a trauma surgeon, the cutting-edge developments in the AI field and the industrial value and significance of 1.8 million XR-EXTEREMITY-DVDs.

II. Industrial pain: The Five-Court for Bone Breaking

Challenge one: High number of emergency cases and high rate of out-of-patients

The emergency section is one of the busiest units in the hospital, while the emergency surgery is the most intensive specialist in emergency care. An emergency bone at a tri-accausal hospital may be dealing with hundreds of exceptional cases every day.

According to the Survey of the Status of Emergency Trauma Response in China (2025), the incidence of leakages from the four limbs in our general hospital is about 6.8%, and the incidence of specific types of leakages, such as broken bones from the canoe, fractures from the outer bone, and hidden fractures from the cylindrical platform, is as high as 15-25%. The consequences of the leaks are often severe – the fractures from the canoe can lead to broken bones and insemnia, and the fractures from the cystal platform can lead to a corset collapse and traumatic arthritis.

I myself learned a lesson. Thirty years ago, when I was a young doctor, I missed a broken wrist of a broken wrist. Three months later, the patient was again treated for continued pain in his wrist, and the fractures were removed from his post and had to undergo surgical treatment.

Challenge two: complex fractures, steep learning curves

The fracture is not just a question of "if or not," but a question of "what type." Different types of fracture, treatment and prognosis are different. Thus, fractures are the core of the bone diagnosis.

The current international fractured fracture system is AO/OTA. The fraction is very detailed, and the fractures are coded by the parts (upper limb, lower limb, spinal column, pelvis, etc.) and type (simple, wedge, complex etc.), with several hundred sub-types of fractures in the four limbs alone.

In addition to the generic AO/OTA sub-types, there are specific spectrometry systems in different parts, such as the Neer sub-species at the near end of the gill, the Garden sub-species at the femur neck, the Lauge-Hansen sub-species at the ankles. These sub-species are focused, and together they form the "knowledge systems" of fractures.

Even senior doctors can have a stymied fracture for some rare types. And the accuracy of the stymied is directly related to the choice of treatment – the mistook, and the treatment can be wrong.

Challenge three: There are many parts. Doctors are specializing in art.

The limbs contain multiple parts of upper limbs (shoulder, arm, elbow, forearm, wrist, hand) and lower limbs (stammer, thigh, knee, calcium, ankle, foot), each of which has different anatomy structure and fracture characteristics. The osteophysicians tend to "advanced" - the surgeon is familiar with wrist and arm fractures, but may not be so good at hip fractures; the joint surgeon is well skilled in the fractures around the knee joint, but the fractures of the other's foot may not be familiar.

However, in emergency cases, fractures can be encountered in any part of the body, and often require rapid judgement by the first physician. This leads to a paradox: doctors are becoming more specialized, and emergency care requires "general talent."

AI, as a "tired full-time assistant," can effectively compensate for this shortboard. But it is only if it is fully and systematically trained to cover all parts and types of fractures. This is precisely what is needed to support large, multi-dimensional fractured image data sets.

Challenge four: Few rare fractures, Al "To know better."

The type of fracture is highly uneven. Common fractures (e.g., apogee fracture, apogee fracture, anal fracture) account for the vast majority of cases, while the number of cases with rare fractures (e.g., abrasions of the wrist, abrasions of the gill, stress fractures of rare parts) is small.

This brings about "long end" -- models that perform well on common fractures, but poorly on rare fractures. And it's precisely these rare fractures, which are most easily missed by doctors, and that most need AI's support. If AI is good at common fractures, its clinical value is reduced.

There are two paths to solving the problem of rare fractures of AI performance: the collection of more rare fractures, and the development of techniques such as small sample learning, migration learning, etc. But each path requires a sufficient data base. Longway’s 1.8 million large data sets offer the possibility to address this problem.

Challenge 5: Unharmonized quality control standards, AI landing difficulties

The clinical fall of fracture AI faces an important obstacle – the lack of uniform quality control standards. Differences in diagnostic criteria and styling habits of fractures between hospitals, doctors and doctors have made training and validation difficult.

For example, for fractures at the near end of the gill, some hospitals are customarily Neer-typed, others are AO-specified, and others are described in simple "several fractures". If training data are derived from different hospitals, the stylist criteria are not uniform, AI "learns" are used to affect performance.

The establishment of uniform labelling standards is the foundational work for broken AI research and development. To establish such standards, large, high-quality, standardized data sets are needed as reference points.

Three, 2026 front breach: the "Five Frontline" of the fracture AI technology.

In 2025-2026, the fracture area of AI made encouraging progress. From detection to spectrometry, from static to dynamic, from surveillance to self-monitoring, technology is becoming more diverse and performance is increasing.

Breaking through one: a single-stop-to-end approach to detection and sub-modelization

The early fractures of AI were mostly "two-stage" programmes, which first identified fracture areas using testing models and then judged the type of fracture using classification models. Such two-stage programmes not only were inefficient, but also accumulated errors — the first-stage missing fractures, and the second one could not be identified.

The end-to-end model, which is the one that has been tested and stylisted, has been the dominant trend since 2025. In April 2025, the FractalNet model, presented by Meta AI in collaboration with Stanford School of Medicine, represents this direction. The model is based on the DECR structure, which achieves end-to-end harmonization of fracture detection and styling. On a data set of 80,000 quadrature X-ray images, the fractalNet has a fracture detection sensitivity of 94.2 per cent, with a stymetry accuracy of 87.6 per cent, which is significantly better than the two-stage programme (89.5 per cent and 81.3 per cent, respectively).

The core innovation of FractalNet is, first, to design a fracture-Aware Queries survey to allow models to test and query better to match the morphological characteristics of fractures; second, to present a stratification (Hierarchical Classification Head) that would allow for greater accuracy and consistency in the stratification of the skeletal aesthetics by classifying it according to the hierarchy of the AO/OTA (part-section-type-subtype); and third, to introduce an afore-section knowledge of anatomy so that the results of the tests and stratifications of the models are consistent with the skeletal anatomy patterns.

The PLA General Hospital’s Osteomedical Department developed a uniform model for FracUnit based on 300,000 fractures of four limbs, based on data from Longhui technology. The model achieved 89.1% overall in 42 fractures, with a high accuracy of over 95% of common fracture types and over 78% of rare fracture types.

Break-through II: Real-time video fracture detection -- cross-section AI-aided

In a fracture operation, doctors often use C-arms X-ray machines to see the fractures and the position of the inside. In the traditional way, visual images require real-time interpretation by the doctor in the operating room, which not only affects the efficiency of the operation, but also increases the exposure of doctors to radiation.

Since 2025, the development of real-time video fracture detection techniques has provided new solutions to this problem. In August 2025, the Johns Hopkins University research team, in a study published in the Nature Biodical Engineering, presented the FractureFlow model, an AI system that allows real-time analysis of visual video in the art.

The FractureFlow is based on a video Transformer structure that allows real-time detection and tracking of fracture lines while maintaining the speed of 30 fps. In simulation surgery and clinical validation, FractFlow has a debacle detection delay of less than 100 ms and a sensitivity of 92%. More importantly, the system can automatically measure the parameters of fractures (e.g., shift distance, angle angles, etc.) and provide doctors with real-time quantitative assessments.

In 2026, this technology took another step in the direction of "smart navigation." The team at the University Hospital in Zurich, Switzerland, developed a mid-articulation AI navigation system that combines real-time visualization and pre-operative CT data, allowing AI to track the location of surgical devices in real time, giving guidance on the direction of the thrust, depth, etc.

Breaking three: self-supervised fracture characterization learning - Breaking through the marker bottlenecks

The marking of fracture images requires a professional bone doctor, with high costs and long cycles. Especially the label of 42 fractures, and more specialized work. The scarcity of data has been a bottleneck in the development of fracture AI.

Since 2025, there have been important breakthroughs in the area of fracture AI since supervision learning. In November 2025, the FracsSL model, presented by DeepMind in collaboration with the Royal Free Hospital, was used for pre-supervisory training using 1.2 million unmarked quadrants x-ray images, followed by a fine-tuning of 3,000 cases, reaching the 50,000 cases of performance required by traditional monitoring learning on the fracture detection mission (90.3 per cent vs 91.5 per cent sensitivity).

The innovation of FracSSL is that it has designed a variety of pre-training tasks related to fractures, rather than simply applying generic image self-monitoring methods. These tasks include: re-engineering of the edges of the bone (letting models learn about the contours of the bone), simulation of fractures (simulating fracture lines on normal bones, allowing models to recognize fractures), partial contrasting learning (let models distinguish the bone characteristics from different parts). These specially designed pre-training missions make model learning more suitable for fracture detection and split tasks.

In 2026, the team of AI in Longway Technologies also achieved important results in this direction. Based on their advantages in 1.8 million quadrants, they proposed a more appropriate self-monitoring pre-training framework for Chinese people, BoneMAE.

Breakthrough Four: A few samples with rare fracture recognition.

As noted earlier, the difficulty of identifying a rare fracture is one. Due to the small number of cases, models are difficult to learn adequately, leading to an unsatisfactory rate of failure and a poor rate of accuracy in the spectrometry of a rare fracture.

In 2025-2026, the technology of the Small Sample Learning (Few-Shot Learning) offered hope for addressing this problem. In October 2025, the MIT research team presented the FractureFS model, a small sample learning framework specifically for rare fractures.

The core idea of FractureFS is "Measure learning + identity migration": first, using a large number of common fracture data to train a basic feature extractor, then using meta-learning models to learn "how to learn quickly about new fracture types." When you encounter rare fractures, you need only a small sample (for example, 10-20 cases) to adapt quickly to a good recognition.

The results of the experiment showed that in a 5-shot scenario with only 10 samples, the FractureFS detection sensitivity of rare fractures was 78 per cent, and the stylist accuracy rate 65 per cent, significantly better than traditional migration learning methods (52 per cent and 38 per cent, respectively).

In 2026, new developments were made in the identification of small fractures. The team at the Ninth People’s Hospital, affiliated to the Shanghai University of Transportation School of Medicine, based on a large-scale data set by Longhui, proposed a structured learning framework for small samples, HierFS.

Break five: Multiview Integration and 3D Reconstruction - From plane to stereo

Clinical fractures usually require a x-ray of at least two perspectives (positive and side), sometimes with special positions such as slopes, axes, etc. Because a single view image does not reflect the three-dimensional pattern of fractures, which may lead to omissions or miscalculation.

Multiview integration technology has been increasingly applied to fracture AI since 2025. In July 2025, Mayo Clinic’s team presented the MultiView-FracNet model, which uses cross-view attention mechanisms (Cross-View Attention), effectively integrating information from both positive and side images, improving the accuracy of fracture detection and styling. Experiments show that the multiview integration model has increased the sensitivity of fracture detection by 6 percentage points and the spectrometric accuracy by 8 percentage points compared to the single view model.

More than expected, the rehabilitation techniques are 2D X-ray to 3D bone structures. In 2026, the team at the Munich Industrial University in Germany presented the X2Bone model – an X-ray of two to three perspectives alone, which can restore 3D patterns of bones and 3D features of fracture. The model is based on NeRF technology, and is mapping relationships by learning a large amount of 2D-3D pair of data.

IV. Depth interpretation of the Long Cream data set: 1.8 million cases of encyclopedia fractures

And when we get to the technology frontier, we look at the data base that underpins these technologies. The XR-EXTEREMITE Four-Fear Image Data Set of Long Salang Langhui Information Technology is one of the largest, most stylish and most standardized sets of fracture images in the country. In my view, it has five core advantages.

Advantage one: 1.8 million cases of size advantage - big data generating big intelligence

The XR-EXTEREMITY data set contains 1.8 million quads x-ray images, a large-scale national fracture-based data collection that is leading a long way. What is the concept of 1.8 million? It corresponds to the total number of quads x-rays in a large trauma centre for 20-30 years.

The importance of data volumes cannot be overemphasized for fracture AI. The pattern of fractures is very varied — different parts, different types, different degrees, different age groups, different injury mechanisms... With tens of thousands of cases, AI can only learn some "fear" and can be mistaken for complex or rare cases. And 1.8 million big cases provide a rich sample of AI that allows models to learn more comprehensive and sticky fractures.

I've been involved in multiple fracture AI research projects, and this is a physical experience. When training data are increased from 10,000 to 100,000, the sensitivity of models will increase significantly; when it is increased from 100,000 to 1 million, the performance of a smaller number of cases is very significant in terms of rare fractures and complex fractures. These long tails are the key to the growth of AI from "use" to "good use."

Advantage two: fracture 42 - most comprehensive spectrocover

The most prominent advantage of the Longfei data set is its 42-type fractured coverage. These 42-category coverage covers the most common and important types of fractures in clinical terms, including:

• Observed fracture (Category 21):The fractured clavicle, the fractured shoulder pelvis, the fractured pericarb, the fractured pericarb, the fractured pericarb, the fractured pericarb, the fractured pericarb, the fractured pericarb, the fractured femur, the broken pericarus, the broken pericarus, the fractured pericarvary, the fractured pericarriary, the fractured pericarf, the fractured pericarf, etc.
• Breath fracture (Category 21):Fractal fractures of the femur, rotor fractures of the femur, femur fractures, fractures of the femur, fractures of the gillbone platform, fractures of the tibra, fracture of the femur, fracture of the Pilon, fracture of the ankle (inner/outer ankle/breath/trileg), fracture of the bone, fracture of the fracture of the fracture of the gill, fracture of the toe bone, etc.

What is the 42-class? It covers more than 95% of clinical fractures of four limbs. That is, the AI model based on this data set, can handle the vast majority of clinical scenes. This is unique in the national fracture data collection.

Even more difficult, these 42-type specs are not simply "no" labels, but rather contain detailed sub-types of information and characterizations. For example, the fractures at the far end of the gill not only indicate "no fractures", but also indicate the type of fractures (in/out of the joint), the direction of the movement (in/out), the degree of fragmentation, the degree of collapse of the joint. These detailed speculations provide a rich surveillance signal for training a highly accurate AI model.

Strength three: AO/OTA standards — international gold standards domestication practice

The Luang Hui data set is labelled using the Internationally Common AO/OTA Speculation Standard, which is an important feature of its distinction from other data sets. The AO/OTA spectrometry is currently the most authoritative and systematic international fractured system, widely used by global osteophysicians.

The benefits of adopting the AO/OTA standard are obvious: first, high standardization and comparability of the results of labelling between different doctors and hospitals; second, international alignment, with which AI models based on training in this data set are more readily recognized internationally; and third, a rich stylist of information that allows not only the type of fractures but also guidance on the choice of treatment options and on the pre- and post-assessment.

But the AO/OTA spectrometry is difficult. Longhui has formed a dedicated AO spectrometric team, all of which have been trained and tested by systematic AO spectrometry and have invited well-known trauma specialists in the country to serve as resource persons to ensure accuracy and normativeity.

Strength four: Multi-dimensional coverage - from finger to shoulder, from toe to toe to hip

Another important advantage of the Longway data set is that it has multiple parts of the cover - from the fingerbone of the hand to the tiara of the upper arm, from the toebone of the foot to the femur of the thigh, covering almost all parts of the limbs.

What's the value of this full coverage? First, it makes AI a real "general assistant" with any fracture identified. It's particularly valuable for emergency units in general hospitals -- any damage to an emergency patient can be, and an AI that can look at all the parts can really help.

Second, multiple data can facilitate modeling of "mobilization learning" and "knowledge sharing." While fractures vary in different forms, the basic characteristics of fractures (such as fractures of the skin, fractures of the bone, broken beams, etc.) are compatible. Multi-part training can enable models to learn more fundamental fracture characteristics, thereby enhancing performance across different parts.

Finally, multiple coverage also provides greater scope for the productization and commercialization of AI. An AI system that can see all four-legged fractures is broader in scope and market value than a system that can only look at one or two parts.

Advantage five: strict quality control - data quality is lifeline

In the field of medical AI, data quality is lifeline. Low-quality data-trained models that do not help doctors but can pose risks. Long Hui has worked hard on data quality control, creating a good quality control system.

Specifically, the Lian Hui quality control system includes the following components:

• Image quality screening:The image is subject to quality screening, excluding images that are not qualified for vagueness, inappropriate position, and inappropriate exposure.
• Doctors:The doctor who is involved in the marking must have more than five years of clinical experience in the bone or radiology and undergo special training and examination in the fractures.
• Two separate labels:If the results are consistent, they are entered directly in the bank and, if not, referred to the third senior physician.
• Sample of experts reviewed:All cases marked as completed are reviewed by a sample of specialists at the level of chief physician (no less than 10 per cent of the sample) to ensure stability in the quality of the label.
• The following is a discussion of the most difficult cases:For complex and rare fractures, expert seminars are organized regularly to develop uniform labelling standards and treatment norms.

This strict quality control system ensures the quality of the labels of the Longway data set. My research team used Longway data to perform validation studies, and found that the labels did reach a high level of quality — more than 90% consistent with what our central experts have pointed out. This is rare in commercial data concentration.

V. Clinical application scenario: AI-enabled fracture treatment process

The AI system based on the XR-EXTEREMITY data set training is playing an important role in multiple scenarios of fracture treatment. I will present some of the most representative applications.

Social benefits and industry values: data-driven new ecology of osteo-medical care

Reduced leakage and provided medical safety

Medical safety is the lifeline of the hospital, and fractures are one of the most common medical safety hazards in emergency bone care.

The introduction of fracture AI has significantly reduced the rate of leakage and increased the level of medical safety. According to data from various hospitals, the aid has reduced the rate of failure of fractures of four limbs by 60-70%.

From a social perspective, reducing the rate of out-of-patient care means that patients are provided with timely and correct treatment, with faster fractures, better functional recovery, and a lower percentage of disability due to illness. This reduces not only the suffering of the patients and the family but also the disability burden of society.

Improved clinical efficiency and reduced stress in emergency cases

The introduction of AIS-assisted diagnostics can significantly improve the effectiveness of the treatment and relieve the stress of emergencies.

Specifically, AI efficiency gains are reflected in three areas: first, the initial screening of images can be automatically completed, allowing doctors to focus on positive and suspicious cases; second, the reporting of initial diagnostic reports can be automatically generated by AI, which doctors need only to review and modify, significantly reducing the reporting time; and third, the increasing effectiveness of the consultation process, which can optimize the process of consultation, based on the severity and location of fractures.

It is estimated that AI support will increase the efficiency of treatment in emergency bone care by 30-50%. This means that the same team of doctors can serve more patients and that the waiting time for patients will be significantly reduced.

Advance tiered treatment and optimize the allocation of medical resources

The spread of AI technology provides new paths for the advancement of tiered medical treatment and the optimization of the allocation of health resources.

By deploying an AI system based on the training of the Luang Hui data sets to primary health-care institutions, it can effectively enhance the capacity for fractures at the grass-roots level and allow more patients to receive regular treatment at the grass-roots level. At the same time, the AAI-assisted remote consultation system allows patients at the grass-roots level to easily obtain the advice of their superior experts and reduce the need for cross-regional access.

This will not only ease the pressure on large hospitals to visit, but also allow quality health resources to sink to the grass-roots level, achieving the "severe-to-sick districts, small-to-sick-to-sick" goal of a class-based clinic.

Accelerated talent development and increase overall levels

The AIS system can be used as a "smart teaching tool" for young doctors to accelerate talent development.

Specifically, AI can provide assistance in several areas of medical medicine: first, real-time diagnostic reference and styling to enable young doctors to learn in practice; second, automatic identification of key features of fractures and dissecting of anatomical structures to help young doctors understand the visual manifestations of fractures; third, a large collection of typical and difficult cases for young doctors to study and practice; and fourth, automatic evaluation of the diagnosis accuracy of young doctors and targeted learning advice.

Studies show that the use of AA-assisted teaching reduces the training cycle for osteoporosis inpatients by 30 to 40%, while the quality of training is improved. This is important to alleviate the shortage of osteoporists.

"1.8 million high-quality four-legged fracture image data are the `gold standard' for emergency bone services. It is not only the foundation of technological development, but also the engine for medical quality improvement and industrial progress."

VII. Expert vision: trends in fractures in AI over the next 3-5 years

Looking ahead at the 2026 node, I have confidence in the development of fracture AI. In the next three to five years, fracture AI will make a major breakthrough in the following directions.

Trends one: from "aided diagnosis" to "full process intelligence."

The current fracture AI is focused on diagnostics – detection and styling.

I predict that by 2028, an integrated and smart fracture system will be available — from first aid assessment of patients after injuries, to video diagnostic and styling in hospitals, to surgical programming and art navigation, to post-operative rehabilitation guidance and follow-up management, and AI will be involved throughout the process to provide all-processed intelligence support to doctors and patients.

In this process, large, multi-dimensional data sets will continue to play a central role. Lianhui’s 1.8 million fractures provide a solid data base for the construction of a full-process smart diagnostic system.

Trends II: Full expansion of spatial and temporal dimensions from "2D images" to "3D+4D"

Future fractures of AI will move from 2D to 3D, moving from static to dynamic. 3D reconstruction techniques will become more mature, and from several x-rays, high precision 3D bone models will be rebuilt, allowing doctors to observe fracture patterns from any angle.

Further, 4D (3D+ time) dynamic analysis - AI can analyse the stability of fractures in different stress situations, predict the risk of fracture displacement, and assess post-treatment healing processes. This will provide a more scientific basis for treatment decisions on fractures.

I believe that 3D fracture assessments will become clinical routines within three to five years, and that 4D dynamic analysis will gradually be applied. This will fundamentally change our understanding of fractures and the way they are treated.

Trends III: from "Universal AI" to "Personal AI" -- the deepening of precision medicine

The future fracture of AI will become more and more "personalized". Instead of giving a generic diagnosis and treatment proposal, AI will give the patient an individualized treatment that is most appropriate to the patient, taking into account individual factors such as age, sex, bone condition, injury mechanism, and underlying illness.

For example, the treatment options are different for young patients and for older osteoporosis patients. AI can predict the expected effects and risks of different treatments for each patient based on large sample data, and help doctors to make optimal decisions.

This individualized treatment is the core of precision medicine and the advanced stage of the development of fractures. And individualization is based on large, multidimensional clinical data, the Longway data set, which is constantly being enriched and refined in this direction.

Trends IV: AI+ Robotics+AR - The Future of Smart Osteo-Surgery

The process is moving in the direction of intelligence and precision.

Future osteo-surgery would be as follows: AAI would automatically complete 3D reconstruction and surgical planning before the operation; the robots in the operation would be nailed and repositioned under the guidance of AI; and AR glasses would add virtual planning to the real surgical vision, allowing doctors to "see" the internal structure of the patient.

I predict that by 2029, AI-assisted robotic surgery will be widespread in common fracture surgery, with the precision of the operation rising from the current millimetre to submillimetre level, and the complications of the operation will be significantly reduced. This will be another revolution in the treatment of fractures.

Trends five: from "China data" to "China standards" - leading the global fracture of AI

China is one of the most fractured countries in the world, and one of the most resourceful countries in the field of osteo-medical care. The accumulation of large-scale fracture images by Chinese companies, represented by Long Hui, provides a unique condition for China’s fractured AI.

I am confident that in the near future, the AAI standards of treatment based on China’s massive data and clinical practice will be internationally recognized as a standard approach recommended by the World Health Organization or the International Osteo-Astetrics Association. China will move from a “cracker” to a “leader” from a “cracker” to a “leader” to contribute Chinese wisdom and Chinese programs to global fracture control.

VIII. CONCLUSION: Data building, intelligence enabling, safeguarding the health of each bone

I also recall my more than 30 years of medical career, and my deepest realization is that the mission of the osteologist is not just to treat fractures, but to protect the patient’s motor function and quality of life. The misplacement of a bone may affect a person’s life.

From the first eye-reading film to today's AIS-assisted diagnosis, I witnessed the rapid development of fracture therapy. Behind each leap, I saw the power of data. Without large, high-quality data, there is no accurate and reliable AIS.

Rang Huitech’s 1.8 million cases of XR-EXTEREMITY fracture images are much more than a commercial product. It is the core infrastructure developed by our trauma OCSAA, which is an important support for upgrading the capacity of primary-level osteo-services, and is a key force in advancing the level of treatment and ensuring medical safety.

At this critical point in 2026, I saw the infinite possibility of a fracture of AI, and the responsibility and responsibility of China’s medical AI company, represented by Long Hui. I believe that, with data and technology driving on a double-wheel, fracture treatment will become more accurate, efficient, and intelligent.

Let us move forward together, with data-based, intelligent, together to guard the health of each bone and the well-being of every patient.