Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio-cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within-group variations are greater than between-group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians’ diagnoses in the near future. The real challenge for AI-based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general.

This study investigated sex-specific psychological mechanisms underlying cyber reactive aggression (CRA) among Chinese college students, addressing a critical gap in understanding how trait anger (TA), hostile attribution bias (HAB), and revenge motivation (RM) differently influence male and female online aggressive behaviors. Using convenience sampling, we collected data from 926 students (371 males, 555 females) across 12 universities in China. Network analysis with mgm package revealed distinct sex-specific patterns: male participants showed strong TA-HAB and RM-CRA associations, while female participants exhibited prominent HAB-CRA relationships. Notably, maternal education levels were positively associated with aggressive behavior in both sexes (males: weight = 0.22; females: weight = 0.14), while left-behind experiences uniquely was associated with higher HAB in males (weight = 0.55). Multi-group structural equation modeling further validated these sex-specific pathways: TA was significantly associated with CRA in both sexes (males: β = 0.40, p < 0.001; females: β = 0.44, p < 0.001), with both HAB and RM mediating the TA-CRA relationship in males, while only HAB served as a significant mediator in females. These findings advance our theoretical understanding of sex-specific aggression mechanisms in digital contexts and suggest that intervention strategies should be tailored differently for male and female college students.