Healthcare is rapidly changing. New technologies are emerging that can benefit the patient, provider and payer alike. Digital transformation has led to new ways of delivering care, including telemedicine and virtual health clinics. Telemedicine provides patients with access to medical advice from a doctor without having to physically see them in person. This not only saves time for the healthcare professional but it also reduces costs for both parties because there are no travel expenses or wait times associated with waiting rooms. Virtual health clinics work similarly by providing a means of delivering quality care through an online platform that connects providers and patients virtually using high-definition video conferencing technology such as Skype or FaceTime.
At the O’Reilly and Intel Artificial Intelligence Conference, Arjun Bansal, vice president of Intel’s artificial intelligence product division and general manager of artificial intelligence laboratory and software, pointed out that the current healthcare industry has three main challenges: The first is that the amount of data is extremely large and continuous increasing; the second is that there are not enough clinicians who are qualified and available to take the role of providing critical care; the third is the time and high cost.
In order to solve these three types of challenges, the healthcare industry needs to use leading technology products to actively deploy solutions through many industry partners in the fields of disease monitoring, clinical environment, imaging analysis, virtual services, and virtual reality assistants.
Below are some of the highly used programs that provide technical support for data processing, promotes and expands the boundaries of the medical field.
In the field of disease monitoring, with the help of predictive models based on machine learning or cognitive systems, doctors can estimate the risk of chronic diseases based on the characteristics of patients, without having to adhere to established care plans or have patients repeat hospital admissions. Such early intervention can greatly reduce the patient’s medical expenses.
Machine learning-based models can also be used in the clinical environment. Common predictive models include the use of electronic medical record data to assess the risk of infection in hospitals, and operating models to predict the probability of patients entering the emergency department.
The use of deep learning to analyze medical images is also one of the important applications of artificial intelligence technology in the medical field. Using deep learning technology to analyze medical images for tumor detection can help radiologists improve reading efficiency.
The application of telemedicine provides enterprises and consumers with richer solutions to improve clinical diagnosis. For example, in the case of remote stroke disease diagnosis, a model based on deep learning can identify early stroke characteristics of patients, thereby improving the accuracy of diagnosis and greatly shortening the diagnosis and treatment time.
The fifth use case of artificial intelligence is to create the next generation of virtual reality assistants. In the future, artificial intelligence can respond to participant interactions in virtual reality sessions. Patients can interact with the virtual environment and observe possible changes in their condition.
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 improve business processes and patient experience. With the further improvement of computing and analysis capabilities, the application scenarios of artificial intelligence in the medical and health field will be more abundant.