As AI enables automatic problem-solving and is highly intelligence, it can be applied to a wide variety of fields in the healthcare, especially for the labour intensive tasks. In China, from the basic medical health records to the complex disease treatment planning and prediction, you can always find the involvement of AI. Here, we choose seven sectors with more mature development and introduced them.
They are CDSS (Clinical Decision Support System), Virtual Assistant, Healthcare Management, DiseaseDiagnosis and Treatment, Robotic Surgery, Medical Imaging and Drug Discovery.
Clinical decision support systems (CDSS) are computer-based programs that analyze data within electronic health records to provide prompts and reminders to assist health care providers in implementing evidence-based clinical guidelines at the point of care.
CDSS can help physicians improve diagnosis efficiency and quality. What’s more, as the inequality in medical resources has become a severe problem in China, CDSS can accelerate the development of the hierarchical medical system and improve the medical service system.
Knowledge system based on medical and health field, human-computer interaction through intelligent voice technology and natural language processing technology.It is mainly applied to mobile terminal, providing users with 7´24 all-day intelligent services through mobile APP and WeChat mini-program. The AI virtual assistant can provide supplementary consultation, healthcare and case tracking services in addition to the doctor's treatment, and can play an important role in the hospital's patient triage.
Compared to the real doctor who often interrupts patients’ statement of the medical condition, the AI virtual assistant allows them to describe their health condition in detail;
As patients don’t need to talk with the doctor face-to-face, and the anonymity is also allowed during the consultation, Patients will feel more comfortable telling "AI doctors" about their sensitive issues, and the doctor-patient communication environment will be more cordial.
Through the integration and application of AI technology in real life, AI in healthcare management can collect, monitor and analyze the data throughout the user’s lifecycle, in order to improve the user’s health intervention and management capabilities;
AI in healthcare management can be divided into two significant parts, which are physical health management and mental health management. The former include mainly the management of chronic diseases, such as cardiovascular diseases and diabetes. China has a large amount of elderly people and a high prevalence of chronic diseases. AI-based healthcare systems can help users self-monitor their health conditions and provide intervention measures when necessary. China has over 95 million patients suffering from the depression and the number continues to increase. AI in healthcare management can help patients relieve their metal stress and give them some advices to control their negative emotions.
AI in healthcare management can also help alleviate the imbalance of supply and demand for medical resources and provide more comprehensive support for the continuous improvement of the population health.
Disease Diagnosis and Treatment
There are more than one hundred types of cancer recognized worldwide. Identifying diseased cancer cells from thousands of cells is a time-consuming and laborious task. AI-based technology can predict the risk of disease occurrence through the gene sequencing and testing and assist pathologists to complete the disease screening efficiently and accurately.
According to the experts, the misdiagnosis rate of cancer in China is around 30%. Due to the unequal distribution of medical resources in China, medical technologies in remote rural areas are less advanced and the level of rural doctors is lower than in urban areas. Besides, Some cancers have obscure symptoms and can be easily confused with other diseases. AI-assisted detection equipment can not only identify diseased cancer cells among a large number of cells, but also minimize the rate of misdiagnosis.
AI-driven surgical robots are computer-manipulated devices that allow surgeons to focus on the complex aspects of a surgery.
Their use decreases surgeons' fluctuations during surgery and helps them improve their skills and perform better during interventions, hence obtaining superior patient outcomes and decreasing overall healthcare expenditures. Considering the management application and the usage cost, AI-assisted surgical robots are currently applied in tertiary hospitals.
There are different types of AI-assisted robotic surgery, for example, heart surgery, joint surgery, gastrointestinal surgery, colorectal surgery, gynecologic surgery, prostate surgery, head and neck surgery, and kidney surgery.
Through deep learning, AI-driven machines can analyze and judge medical images, in order to assist radiologists in their diagnostic and treatment work, help them to improve their efficiency, conduct qualitative and quantitative analysis and discover hiden lesions.
AI-based medical imaging technology can help doctors reduce their high workload, thus reducing the misdiagnosis. According to the public data, in 2016, there were estimated 7.54 billion medical visits for radiology in China but the number of radiologists was 158,000. And the need for radiologists is stably increasing. AI can take repetitive tasks away from radiologists to help them relieve their pressure and reduce the workload.
AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design; More specifically, AI can empower the drug discovery from the following five areas: drug design, polypharmacology, chemical synthesis, drug repurposing and drug screening.
In 2015, the State Council of China reformed the medicine review and approval system and encouraged Chinese pharmaceutical companies to develop more novel drugs. But the research and development of innovative drugs is a time-consuming and costly process. Ai can help accelerate the process and reduce the R&D cost