The AI process in the healthcare industry seems to be facing a structural contradiction: single-point tools are constantly emerging, yet it is difficult to penetrate the complexity of the industrial scene. When GM's big models poured into the medical circuit, most products were limited to fragmented applications such as auxiliary diagnosis and intelligent customer service, and lacked deep adaptation to the entire link of pharmaceutical circulation, commercial decision-making, and terminal services. Data silos, fragmented scenarios, and high compliance thresholds have made the implementation of AI on the industrial side always “win over the seat”.

The AI process in the healthcare industry seems to be facing a structural contradiction: single-point tools are constantly emerging, yet it is difficult to penetrate the complexity of the industrial scene. When GM's big models poured into the medical circuit, most products were limited to fragmented applications such as auxiliary diagnosis and intelligent customer service, and lacked deep adaptation to the entire link of pharmaceutical circulation, commercial decision-making, and terminal services. Data silos, fragmented scenarios, and high compliance thresholds have made the implementation of AI on the industrial side always “win over the seat”.