Author: Zen and the Art of Computer Programming
1 Introduction
In recent years, with the rapid development of Internet technologies such as artificial intelligence and big data, breakthroughs have also been made in the medical field. On the one hand, big data technology represented by human body recognition has become the infrastructure of modern medical care; on the other hand, biomedical diagnostic models based on artificial intelligence technology are evolving towards a more advanced and accurate direction. These developments have driven the medical community to continually pursue higher levels of technology to improve patient satisfaction while reducing costs. Therefore, in this technological revolution, a series of new medical devices and services have been created, including medical assistant robots, digital twins, and even self-learning intelligent diagnostic systems. In recent years, driven by the popularity of electronic medical records and mobile payment, the development of intelligent medical robots has become an indispensable part. Based on the latest technology, this article focuses on the technological development of medical robots, and analyzes its development trends, key technologies and future development directions.
2. Background At present, the overall technological progress in the medical field has formed a unique and coordinated development model. Early surgical equipment often used wooden structures, which were less efficient, but the surgical process was simple and convenient. Later, various medical devices based on computer control and computer-assisted treatment technology gradually replaced surgical equipment, mainly for difficult and complex operations, while increasing treatment efficiency. Most modern medical devices are driven by high-performance computing platforms, which can achieve more advanced functions. Artificial intelligence (AI) and machine learning (ML) techniques are used in various medical applications, such as image recognition, text analysis, speech recognition, bioinformatics, etc. By learning a large number of cases and related symptoms, the machine learning model can automatically judge the patient's disease and make a corresponding diagnosis, helping doctors quickly discover and diagnose the disease, and shortening the treatment time of the patient. With population growth and urbanization accelerating, AI-based healthcare systems have become an important part of the medical field.
However, due to the increasing complexity, industrialization, and standardization of the medical industry, the research and development of medical robots is also facing many challenges. How to enable medical robots to accept high-quality input data, generate high-quality output results, and treat patients