This review describes the concept of artificial intelligence, introduces the working mechanism and the main structure of medical expert system, as well as the development history of medical expert system at home and abroad and its applications in the medical field. The concept of machine learning, commonly used algorithms and its clinical applications in medical diagnosis are briefly described. It mainly introduces the application of artificial intelligence in neurology. The advantages and disadvantages of artificial intelligence system in medical field are analyzed. Finally, the future of artificial intelligence in the medical field is forecasted.
Artificial intelligence in robot system is mainly divided into two types: endoscopic robot system and intracavitary robot navigation system. The endoscopic robot system can effectively shorten the time of vascular anastomosis and occlusion during vascular bypass surgery, while the intracavitary robot navigation system has good localization and real-time observation function. Moreover, it has significant advantages in complex lesions and special anatomical locations. High cost and complicated equipment debugging are the main factors that limit the wide application of robot systems. Artificial intelligence represented by robot system has obvious advantages and broad prospects in the field of vascular surgery, but more research is needed to improve its shortcomings and to further clarify its standard operation and long-term results.
Tuberculosis is one of the major infectious diseases that seriously endanger human health. Since 2014, it has surpassed human immunodeficiency virus/acquired immunodeficiency syndrome as the first infectious disease in patients with single pathogens. China is the third-largest country in the world in terms of high burden of tuberculosis. In 2016, there were about 900 000 new cases of tuberculosis in China. China is facing a severe tuberculosis epidemic, especially for the early diagnosis of tuberculosis and misdiagnosis of tuberculosis, which leads to delay in treatment and the spread of tuberculosis. With the application of artificial intelligence in the medical field, machine learning and deep learning methods have shown important value in the diagnosis of tuberculosis. This article will explain the application status and future development of machine learning and deep learning in the diagnosis of tuberculosis.
Big data technology is an inevitable result of the information age, which not only promotes the development of biomedical science, but also opens up new paths for the development of traditional Chinese medicine (TCM). This paper introduced the application status of big data technology in the field of TCM in recent years, and put forward some thinkings and prospects so as to provide new insights and methods for the future development direction of TCM.
The technical combination of artificial intelligence (AI) and thoracic surgery is increasingly close, especially in the field of image recognition and pathology diagnosis. Additionally, robotic surgery, as a representative of high-end technology in minimally invasive surgery is flourishing. What progress has been or will be made in robotic surgery in the era of AI? This article aims to summarize the application status of AI in thoracic surgery and progress in robotic surgery, and looks ahead the future.
ObjectiveTo explore the application of artificial intelligence in postoperative follow-up of day surgery patients, so as to establish an intelligent medical framework, promote the intelligent process of hospitals, and improve the management level of day surgery.MethodsThe artificial intelligence phonetic system was carried out by the Day Surgery Center, Renji Hospital, Shanghai Jiaotong University School of Medicine on June 1st, 2018. Through the system, the artificial intelligence voice system based on speech and semantic recognition technology was adopted to connect the data of the information center in the hospital to carry out postoperative follow-up of day surgery patients. We selected the 2 245 patients followed up by the artificial intelligence phonetic system from June 1st to November 30th 2018 (the AI follow-up group) and the 2 576 patients followed up by the traditional manual method from January 2nd to May 31st 2018 (the manual follow-up group), to compare the telephone connection rate, information collection rate, and call duration between them.ResultsThere was no statistically significant difference in telephone connection rate (85.70% vs. 86.68%) or information collection rate (98.86% vs. 98.48%) between the AI follow-up group and the manual follow-up group (P>0.05); but there was a statistically significant difference in call duration between the AI follow-up group and the manual follow-up group [(165.48±43.28) vs. (135.37±36.31) seconds, P<0.05], and the AI follow-up group had a longer call duration.ConclusionsThe application of artificial intelligence phonetic system in surgery has a good performance in call connection rate and information collection integrity. It plays an active role in improving efficiency, extending medical services and strengthening medical safety in the management of day surgery.
For the past few years, artificial intelligence (AI) technology has developed rapidly and has become frontier and hot topics in medical research. While the deep learning algorithm based on artificial neural networks is one of the most representative tool in this field. The advancement of ophthalmology is inseparable from a variety of imaging methods, and the pronounced convenience and high efficiency endow AI technology with promising applications in screening, diagnosis and follow-up of ophthalmic diseases. At present, related research on ophthalmologic AI technology has been carried out in terms of multiple diseases and multimodality. Many valuable results have been reported aiming at several common diseases of ophthalmology. It should be emphasized that ophthalmic AI products are still faced with some problems towards practical application. The regulatory mechanism and evaluation criteria have not yet integrated as a standardized system. There are still a number of aspects to be optimized before large-scale distribution in clinical utility. Briefly, the innovation of ophthalmologic AI technology is attributed to multidisciplinary cooperation, which is of great significance to China's public health undertakings, and will be bound to benefit patients in future clinical practice.
With the development of society and the progress of technology, artificial intelligence (AI) and big data technology have penetrated into all walks of life in social production and promoted social production and lifestyle greatly. In the medical field, the applications of AI, such as AI-assisted diagnosis and treatment, robots, medical imaging and so on, have greatly promoted the development and transformation of the entire medical industry. At present, with the support of national policy, market, and technology, we should seize the opportunity of AI development, so as to build the first-mover advantage of AI development. Of course, the development and challenges are coexisted. In the future development process, we should objectively analyze the gap between our country and developed countries, think about the unfavorable factors such as AI chips and data problems, and extend the application and service of AI and big data to all links of medical industry, integrate with clinic fully, so as to better promote the further development of AI medicine treatment in China.
The paper summarizes three revolution trends of medical service mode in the age of 5th generation mobile networks (5G), including artificial intelligence & intelligent medical service, internet of things & internet hospital, and intelligent hospital. Artificial intelligence & intelligent medical service mainly covers artificial-intelligence-assisted diagnosis, artificial-intelligence medical decision-making, and artificial-intelligence-assisted new drug research & development. Internet of things & internet hospital mainly covers internet hospitals, internet care, cloud pharmacies, and medical imaging clouds. Intelligent hospitals mainly cover intelligent clinics, intelligent wards, and intelligent management. The revolution trends count on not only techniques such as 5G, but also the support and cooperation of the government and society. The risk of information and data leak needs attention.
Pathological myopia is one of the most challenging clinical diseases in the field of ophthalmology. The accurate definition, standard classification, disease evolution mechanism and disease prevention and treatment strategies are still under investigation. The development and application of artificial intelligence provides a powerful tool for the analysis of pathological myopia related data. More and more accurate data information is obtained in the clinical work and clinical research of pathological myopia through the standardized collection and acquisition of the fundus image data, the automatic segmentation and quantitative analysis of the fundus physiological structure, the automatic detection and analysis of the pathological myopia classic lesions and the clinical diagnosis and treatment decision aid, which helps ophthalmologists to understand the pathogenesis and evolution of pathological myopia.