Purpose/Significance To analyze and outline the training process and theoretical model of ChatGPT, and to provide references for medical research. Method/Process The literatures on relevant models and process of GPT-1 released since 2018 are systematically reviewed. The core process, theoretical model, and innovative aspects of ChatGPT are analyzed. The three-level technical components of ChatGPT are examined, which include pre-training supervision, automatic evaluation, and proximal policy optimization (PPO) for reinforcement learning.Combining with the needs of medical research, the optimization direction of artificial intelligence (AI) technology for medical information application is analyzed. Result/Conclusion The breakthroughs in the application of ChatGPT technology result from effective combinations of processes, algorithms, and models through continuous iteration and accumulation. The models and research methods of ChatGPT can be applied in automated reading and knowledge extraction of medical literature, gene and disease risk assessment and so on.