# Requirements for Dental CAD Software: A Survey of Korean Dental Personnel
## 3 Results
### Comparison of the Importance of Survey Items within Each Category  
Table 2 shows the importance of survey items within each of the six categories. In Category 1 (considerations when purchasing dental CAD software), design quality was considered the most important, followed by price, diversity of features, ease of learning, compatibility with various scanners, and CAM equipment (Table 2, \(p<0.05\)). In Category 2 (dental prosthesis design process), design convenience and reproducibility were considered the most important, whereas the diversity of the design tools and compatibility with various items of CAM equipment were considered the least important (Table 2, \(p<0.05\)). In Category 3 (dental CAD software features), margin line setting and virtual articulator function in the crown design section showed the highest and lowest scores, respectively, whereas the border line setting and artificial tooth arrangement functions in the denture design section exhibited the highest scores (Table 2, \(p<0.05\)). In Category 4, Section 1 (AI function in crown design), automatic margin setting was considered the most important, whereas automatic shade detection and contact point formation were considered the least important (Table 2, \(p<0.05\)). An automatic artificial tooth arrangement, automatic border line formation, and automatic occlusion adjustment exhibited the highest scores in Category 4, Section 2 (AI function in denture design), whereas automatic gingival formation, automatic relief, and block out exhibited the lowest scores (Table 2, \(p<0.05\)). In Category 5, Section 1 (positive impact of AI-based dental CAD software), increase in convenience was considered the most important, whereas marketing strategy, future prospects for the dental field, and labor cost reduction were considered the least important (Table 2, \(p<0.05\)). In Category 5, Section 2 (negative effects of AI-based dental CAD software), wage reduction, increase in the unemployment rate, and reduced use of conventional workflow showed the highest scores (Table 2, \(p<0.05\)). Finally, in Category 6 (requirements for purchasing AI-based dental CAD software), price, compatibility with existing software, implementation of meaningful functions in clinical practice, and design speed and convenience exhibited the highest scores (Table 2, \(p<0.05\)). Figure 1: Demographic characteristics of survey participants. * Indicates significant differences within each category or section based on the results of one-way ANOVA testing (\(p<0.05\)). Identical capital letters within each category or section indicate no statistically significant differences (\(p>0.05\)). \end{table}
Table 2: A comparison of the importance of survey items within each category. Comparison of the Importance of Each Survey Item by Age, Dental Personnel, and Work Experience Groups  
Table 3 shows the importance of each survey item in terms of age, dental personnel, and work experience groups. In Category 1 (considerations when purchasing software), branding importance differed significantly by dental personnel (\(p\) = 0.017), with dentists and dental technicians allocating significantly higher importance compared with the other dental personnel (\(p\) < 0.05). Moreover, the importance of functional diversity significantly differed by dental personnel (\(p\) = 0.001), with dental technicians and dental-related workers assigning significantly greater importance compared with the other dental personnel (\(p\) < 0.05). A significant difference was also observed in the importance of acquaintance recommendation (\(p\) = 0.015), with dentists and dental-related workers placing greater importance on it than the other dental personnel groups (\(p\) < 0.05). In Category 2 (dental prosthesis design process), significant differences in the importance of design speed were observed between dental personnel (\(p\) = 0.026), with dentists and dental-related workers placing greater importance on this factor than the other occupational groups (\(p\) < 0.05).