Artificial Intelligence (AI) is an interdisciplinary subject developed on the basis of computer technology, cybernetics, mathematics, philosophy and brain science. The purpose of AI is to study new ways to extend the intelligence of human brain in various fields. In recent years, the rapid development of AI technology has brought innovation to medical science and health care. During the pandemic of coronavirus disease 2019 (COVID-19) AI has been widely used in epidemiological investigation and outbreak prediction, clinical diagnosis and treatment, hospital management, research and development of new drugs and vaccines. The application of AI has reduced the clinical workload and the consumption of medical resources, greatly assisted the battle against COVID-19.This article introduces the progresses on the applications of AI technology to provide information for its further application in the fighting against COVID-19.

Multiple injuries caused by trauma have high rates of disability and mortality and are difficult to treat, which have a negative impact on the patients, their families and the society. At present, the medical model of trauma treatment is still inadequate, and the treatment of trauma patients faces great challenges. Artificial intelligence (AI) is an intelligent technology based on machine learning, reinforcement learning and deep learning algorithm, and it has been applied to the treatment of patients with trauma. Its efficient and accurate computer vision, planning and decision-making, and big data statistical analysis not only improve the safety and efficiency in the treatment of trauma, but also reduce the workload of clinicians, which makes up for the deficiency of the traditional model of trauma care. After screening the recent studies of AI in trauma care, the authors review its application in emergency triage, diagnosis, treatment and prevention of war trauma, in order to introduce the latest research progress of AI in trauma care and provide references for future developments.

In recent years, artificial intelligence-related technologies have been deeply combined with many medical fields, and the intersection of medicine and engineering has become a hot topic. There are problems with heavy data and difficulty making decisions in orthopedic disease diagnosis and treatment. Machine learning is an important method of artificial intelligence. Since it can automatically analyze and predict medical big data, it is widely used in the field of orthopedics. It also assists physicians in completing disease diagnosis and treatment efficiently. In this review paper, the application and progress of machine learning in preoperative, intraoperative, and postoperative diagnosis and treatment in orthopedics are reviewed, providing new ways for exploring more rational diagnosis and treatment strategies.

Orthopaedic surgery is complex and delicate. The Orthopaedic Navigation System is developed to provide an augmented reality three-dimensional (3D) visualization environment to improve treatment outcomes by analyzing preoperative, intraoperative and postoperative data. With the rapid development and clinical application of digital technology, artificial intelligence technology has been introduced into orthopaedic intraoperative navigation system. Artificial intelligence, combined with instrumentation devices and imaging technology, enhances the visualization capabilities of orthopaedic surgeons, allowing them to receive real-time feedback and guidance during surgery, which in turn provides optimal clinical decision-making. The application of artificial intelligence to intraoperative orthopaedic navigation also improves the repeatability of procedures and reduces the incidence of human error. This paper reviews the current status of the application of artificial intelligence in orthopaedic intraoperative navigation, and introduces the basic concepts of artificial and the development of image alignment, real-time tracking, and 3D visualization techniques based on artificial intelligence, as well as discusses the current limitations and shortcomings.