At the recent O'Reilly and Intel Artificial Intelligence conferences, Arjun Bansal, vice president of Intel's Artificial Intelligence Products Division and general manager of artificial intelligence laboratories and software, pointed out that there are three major challenges in the medical industry. The first is that the amount of data is particularly large. And it is constantly increasing; second, there are not enough clinicians, and the third is high time and cost. In order to solve these three types of problems and challenges, Intel is actively deploying labor in the fields of disease surveillance, clinical environment, imaging analysis, virtual services and virtual reality assistants with a combination of leading artificial intelligence technology products and many domestic and foreign industry partners. Intelligent solutions provide technical support for data processing in the medical field, promote intelligence in the medical field, and expand the boundaries of precision medical fields.
Disease surveillance
In the field of disease surveillance, with predictive models based on machine learning or cognitive systems, doctors can predict whether or not they will develop a chronic disease based on their characteristics, without having to adhere to a defined care plan or have patients repeat hospitalization. Such early intervention can greatly reduce the patient's medical expenses. The Montefiore Health System deploys a data analysis platform based on the Intel® Xeon® processor that analyzes large amounts of raw data in real time to help clinicians determine the best treatment plan for their patients. At the same time, the normative model can be used to identify the risk of respiratory failure in patients, so that health care providers can take early warning measures to intervene, save lives and save resources.
Clinical environment
Machine learning-based models can also be used in clinical settings. Common predictive models include the use of electronic medical record data to assess the risk of infection in hospitals, and the ability to predict the probability of a patient entering the emergency department through operational models. The rapid response team model developed by Intel and Sharp Medical can predict which patients need rapid response group intervention based on data from the electronic medical records. At the same time, through the model, the hospital can quickly find the corresponding emergency personnel and equipment, thereby reducing response time. In experiments using historical data to test models, the accuracy of patients requiring rapid response group intervention was estimated to be approximately 80%.
Imaging analysis
The use of deep learning to analyze medical images is also one of the important applications of artificial intelligence technology in the medical field. In this regard, Intel has partnered with industry partners to analyze medical images using deep learning techniques for tumor detection. In collaboration with GE Healthcare, GE Healthcare uses the Intel® Xeon® scalable platform to reduce the total cost of ownership of imaging equipment by 25%. Used in conjunction with GE Healthcare's imaging solutions, the Intel Xeon scalable platform helps radiologists improve reading efficiency, with the first image display time dropping below 2 seconds and all study load times down to less than 8 seconds.
Virtual service
The fourth use case of artificial intelligence is a virtual service represented by telemedicine. Telemedicine applications provide a richer solution for businesses and consumers, and in-patient medical robot InTouch Health is one of the representatives of innovative solutions. At the same time, the resulting video dataset can be used to develop artificial intelligence solutions that complement clinical diagnostics. For example, in the case of remote stroke disease diagnosis, a deep learning-based model can identify early stroke characteristics of the patient, which in turn increases the diagnostic accuracy and greatly reduces the time of diagnosis.
Virtual Reality
The fifth use case for artificial intelligence is to create the next generation of virtual reality assistants. In the future, artificial intelligence can respond to participants' interactions in virtual reality sessions. Patients can interact with the virtual environment and observe changes that may occur. In surgical training, artificial intelligence can be used to analyze images to identify top surgeons' best practices that can be fed back into the simulation and can be continuously improved over time.
In short, digital transformation has brought new opportunities to the healthcare sector. In the process of transformation, medical organizations should use data as a core capability to enhance business processes and patient experiences. With the further improvement of computational analysis capabilities, the application scenarios of artificial intelligence in the field of medical health will be more abundant.
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