Generative artificial intelligence （AI）， particularly the ChatGPT series， has great application potential in the medical field. Several products have been introduced into the market， triggering widespread attention across society. Although medical ChatGPT has numerous advantages， its internal technical flaws based on algorithms， data， and platforms may bring about representative external application dilemmas， such as the allocation of legal liability， medical ethical disputes， medical dispute handling， and intellectual property protection. However， China’s current legal norms may not be able to address these issues effectively. To effectively address these contradictions， it is necessary to govern its internal technical flaws through the concept of good governance and to regulate its external application challenges based on the principles of fairness， accountability， remedy， and phased implementation.

Objective To analyze the effect of flipped class mode based on grouping in pathology teaching.Method To select four classes of clinical medical undergraduates as the objects of the study,and divide them into control group and experimental group randomly.Flipped class mode was used in control group (60 people),and flipped class mode based on grouping was used in experimental group (60 people).The effect of teaching was evaluated by test results and e-questionnaire results.The data were collated after the entry of SPSS 20.0,and the data comparison between groups was performed by t test.Result Test results and e-questionnaire results of the experimental group (28.1 ± 0.7) were much better than those of the control group (27.5 ± 0.9),and the difference was statistically significant (P=0.008).It improved the learning interest,self-confidence,team spirit and ability to analyze and solve problems of the students in the experimental group.Conclusion Flipped class mode based on grouping can improve the teaching quality and the self-confidence and team spirit of the students at the same time,and can lay a solid foundation for the clinical work in the future.