There are many applications of AI in the medical industry, such as swallowable mini smart thermometers, surgical robots, wearable devices, etc. The main artificial intelligence medical products that have been put into use are medical robots, intelligent drug research and development, intelligent diagnosis and treatment, intelligent image recognition, and intelligent health management. At present, most medical AI applications are based on tree concepts or establishing databases and algorithm models.
The application of AI in healthcare can be divided into three aspects:
The first aspect is drug research and development. The process of selecting compounds that are active and safe for clinical trials may be cumbersome and require a lot of labor costs. AI can save labor costs in this process;
The second aspect is the diagnosis and treatment assistance of doctors. Due to the lack of doctor resources, AI can improve the efficiency of doctor diagnosis and treatment. For example, it helps doctors identify which lesion is present on CT scans, including simulating the doctor's diagnostic thinking process during the diagnosis process;
The third aspect is genetic characteristics, from digital analysis to application, AI can simulate when the specific response of a drug to the human body is uncertain.
Due to the maturity and technological risks of the products, many medical AI products developed cannot be put into large-scale use, and their usage is also relatively limited.
The biggest advantage of AI is its efficient calculation and precise analysis and decision-making, which can significantly improve work efficiency, unleash medical productivity, and directly address the pain points of resource scarcity and high costs in the medical industry. The author believes that in the period when technology is not yet very mature, the main role of medical AI products is to carry out auxiliary activities to improve efficiency. The era of large-scale replacement has not yet arrived, but with the progress of technology in the future, there is a possibility of large-scale deployment.