Specialized Medical AI Platforms: Driving Healthcare AI from Experimentation to Application
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This article focuses on medical artificial intelligence platforms, analyzing their key construction points, critical application factors, and comparisons between different platform models.
With the continuous advancement of artificial intelligence technology, specialized medical AI platforms have gradually emerged. It is recommended to adopt professional integrated platforms to save effort in platform setup and debugging, allowing more focus on model training and system application. The developed AI systems will also exhibit high reliability and efficiency.
Against the backdrop of future healthcare integration, platforms should feature loose coupling and strong compatibility to meet the integration needs between AI systems, medical equipment, and hospital information systems, thereby enhancing the performance of medical AI systems.
Based on establishing professional medical AI platforms in hospitals, close collaboration with clinical departments is essential to select suitable disease types for diagnostic and therapeutic system development, improving diagnosis and treatment outcomes.
Starting with the development and application of medical imaging AI systems, further integration of diverse data types such as medical records, test results, and patient daily health monitoring data can build richer and more comprehensive healthcare big data, laying a solid foundation for developing more advanced AI systems.
Medical AI platforms consist of three layers: data resource layer, AI platform layer, and medical application layer.
(1) Data Resource Layer provides foundational data by collecting medical imaging data and patient records from various departments, breaking down data silos between systems to establish a data foundation for AI platforms.
(2) AI Platform Layer comprises computing power, open-source frameworks, algorithms, and technologies. Computing power ensures the operational speed of AI platforms.
Taking lung nodule medical imaging data as an example, each patient typically has 20-30 scans. When automatically identifying lung nodules, computer vision models like residual neural networks are commonly used. These enable training of neural networks with dozens or even hundreds of layers, which places high demands on computing power. Most solution providers offering medical solutions are companies with comprehensive healthcare IT experience. These companies, having operated in the medical field for years, possess deep familiarity with healthcare workflows and natural advantages in developing AI medical imaging applications.
These companies have profound understanding of hospital scenarios and physician needs, enabling rapid integration of AI technology with requirements to create physician-oriented products. Additionally, their extensive customer networks and stable hospital partnerships facilitate easier deployment of AI systems within medical institutions.
Currently, some medical AI applications are embedded in medical devices, using AI to optimize equipment performance.
For example, motion capture technology can assess patient rehabilitation progress, providing visualized data displays to support doctors in creating rehabilitation plans. Other applications leverage data center resources like imaging and medical records to enhance auxiliary imaging diagnosis and clinical decision support.
These companies expand into new medical AI domains by building on their existing businesses and technical expertise.
Medical data providers find deep learning particularly suitable for large-scale data applications, such as routine examinations generating massive datasets.
The ability to improve diagnostic efficiency and accuracy is crucial for early disease detection and treatment, especially in regions where doctor shortages cause prolonged delays in imaging and pathology assessments.
Primary hospitals, specialists, provincial hospitals, and emerging independent imaging centers as medical imaging providers have urgent needs for AI-assisted diagnostic systems.
China's medical imaging data is transitioning from traditional films to digital formats. Imaging data has relatively low signal-to-noise ratios, and even with extensive professional training, diagnostic conclusions are often influenced by physicians' experience, fatigue levels, and patience.
Deep learning uses unsupervised or semi-supervised feature learning and hierarchical feature extraction algorithms to replace manual feature acquisition. Despite unavoidable factors like varying data quality, this approach reduces diagnostic inconsistencies caused by human factors and lowers misdiagnosis rates.
Building medical AI platforms helps healthcare institutions improve service quality, balance medical resources, and alleviate treatment pressures, especially in resource-scarce regions.
Medical institutions can choose different construction models based on their IT capabilities to enhance services. The massive data volumes demand significant computing time, making high-performance computing platforms essential for reducing processing time, improving efficiency, and shortening patient wait times—critical factors in clinical applications.
Hospitals utilize vast medical data to build standalone AI healthcare platforms separate from operational systems. These integrate multi-source heterogeneous data from various systems, applying natural language processing to transform clinical descriptions into structured language, creating medical knowledge graphs that preserve and replicate valuable expertise to underserved areas.
Independent medical platforms have longer construction cycles and involve interfacing with numerous systems, presenting greater challenges.
To develop effective algorithm models, medical data annotation is typically required.
Even with unsupervised or semi-supervised learning, early stages still need annotated medical data for model training. Data annotation is time-consuming, high-threshold work requiring skilled professionals—currently mainly experienced physicians performing manual annotations.
Meanwhile, collaboration among healthcare IT vendors needs improvement. As the 'lifeblood' of medical development, data must flow freely across systems. Breaking down silos between hospital systems is key to advancing medical AI.
Existing hospital IT systems supporting daily operations are structurally complex with high modification costs, making it difficult for emerging AI diagnostic systems to replace them.
Most AI systems provide service interfaces integrated with existing systems, combining AI technology with legacy infrastructure.
For medical imaging, suspected lesion outputs don't require opening separate systems but appear as alerts within existing Picture Archiving and Communication Systems (PACS).
Such embedded AI modules reduce development costs and, more importantly, maintain physicians' existing workflows and habits, lowering training requirements. AI systems preserving established practices gain higher hospital acceptance and utilization rates.
Embedded AI platforms don't rely on original system data. With growing data importance, this approach secures legacy system databases while enhancing vendor collaboration, facilitating AI adoption in healthcare.
Three critical elements must be addressed in establishing and applying medical AI systems: data, platform computing power, and deep learning algorithm models.
Medical AI systems require healthcare big data as foundation, using machine learning to develop intelligence for auxiliary diagnosis and treatment functions.
Healthcare big data primarily includes medical textbooks, patient records (especially disease-specific cases), digital medical imaging, and academic papers.
For medical imaging AI systems, digital imaging data—including CT, MRI, ultrasound, and pathology images—is essential as the raw material for machine learning.
Since medical records and digital imaging data are considered intellectual property of hospitals, the principles and management methods for the intellectual property rights of AI systems need to be continuously explored in practice.
Medical data is diverse, sourced from various channels, and comes in vastly different formats. Therefore, efficiently collecting, integrating, and processing data to ensure the training and learning of AI models is a fundamental challenge in developing AI systems.
Currently, when deploying AI-assisted diagnostic imaging systems in hospitals, it is often necessary to retrain the models using the hospital's own imaging data and fine-tune parameters to meet specific needs.
This is because, in terms of imaging data, inconsistencies in standards across hospitals—such as contrast agent dosage or equipment parameter settings—result in variations in image grayscale. Consequently, imaging data for the same patient may differ between hospitals, leading to different model parameters when used for machine learning.
To enhance the applicability of AI systems, it is crucial to rapidly integrate multi-source data during development, thereby training more accurate and widely applicable AI models.
Beyond data processing, selecting or developing deep learning algorithms is another major challenge in the development process.
While numerous deep learning algorithms exist, they often cannot be applied directly. Instead, they require modifications and refinements before being used in data training. Continuous improvement during training is necessary to enhance the precision of the algorithm models.
Thus, choosing or developing suitable algorithms, as well as establishing platforms for algorithm adjustment and improvement, is a key factor in the success of AI systems.
Since AI systems are still in their early stages, the algorithms used in current hospital applications often do not fully align with real-world needs and require ongoing refinement. Improving algorithm models is a critical task in advancing AI systems toward greater accuracy. As shown in surveys, most AI systems in hospitals currently require varying degrees of algorithm upgrades or improvements.
Building a high-performance computing platform is another fundamental element for successful AI development. Deep learning requires massive amounts of data input for model training, which in turn demands extensive computational power to develop intelligent models. Therefore, the computing capability (or "compute power") of an AI platform is a decisive factor in its success.
Currently, AI computing platforms primarily rely on GPU chips, with medical imaging AI systems particularly dependent on GPUs for training and learning. Some AI systems also use CPUs, FPGAs, or high-performance processors (e.g., TPUs).
Major server manufacturers, such as Dell, H3C, Lenovo, and Inspur, have developed servers tailored for machine learning and AI system operations. NVIDIA has also introduced the DGX supercomputer for AI development and deployment.
Most AI computing systems today are built on open-source platforms, which are then customized to meet specific product needs.
Popular open-source frameworks include TensorFlow, Distributed Machine Learning Toolkit (DMTK), and Caffe. Customizing these platforms requires strong development expertise, as the quality of development directly impacts the computational power, efficiency, and accuracy of the AI system.
NVIDIA's Clara platform, for example, effectively integrates GPU computing power with various machine learning models, providing specialized support for deep learning and AI operations, as well as tools for processing imaging data.
The advancement of medical AI platforms heavily depends on the existing IT infrastructure of hospitals. Data is the foundation of AI development, and hospitals require extensive historical data to support research, medical record analysis, and treatment planning.
The willingness of healthcare IT vendors to collaborate also influences the adoption of AI technology in medical institutions. Data, as the "lifeblood" of medical progress, must flow freely across systems. Breaking down barriers between hospital systems is critical for the growth of medical AI.
The medical field imposes higher demands on AI technology. Medicine is a systematic and comprehensive discipline, yet many AI companies focus on single-disease recognition, which, while academically valuable, has limited practical clinical utility. Healthcare institutions find single-disease AI diagnostics less appealing.
For AI to deliver commercial value, it must meet basic clinical needs—such as identifying most diseases of a specific organ or a range of related conditions—thereby driving further research and sustained economic benefits. Additionally, product design must align with doctors' workflows and diagnostic habits.
For instance, ultrasound diagnostics require real-time imaging analysis, demanding AI systems capable of instantaneous processing. Traditional post-acquisition analysis contradicts doctors' operational routines. Thus, the advancement of AI in healthcare depends not only on technological progress but also on professionals with deep industry knowledge.