Commercial artificial intelligence lateral cephalometric analysis: part 1-the possibility of replacing manual landmarking with artificial intelligence service.

Artificial intelligence (AI) technology has recently been introduced to dentistry. AI-assisted cephalometric analysis is one of its applications, and several commercial AI services have already been launched. However, the performance of these commercial services is still unclear. This study aimed to determine whether commercially available AI cephalometric analysis can replace manual analysis by human examiners. Eighty-four pretreatment lateral cephalograms were traced and examined by two orthodontists and four commercial AIs, and 13 commonly used cephalometric variables were calculated. Then, the Bland-Altman analysis was conducted to evaluate systematic and random errors between examiners. The interchangeability of an AI was determined if the random errors of the AI were smaller than the clinically acceptable limits derived from the random errors between human examiners. Finally, the inter-examiner reliability index was calculated, and Cohen's kappa was determined to assess the actual classification reliability of each examiner. The systematic errors of the AIs were clinically insignificant in general. However, the random errors of the AIs were approximately twice those of human examiners, which did not satisfy the interchangeability condition. Furthermore, even though the reliability indices of the AIs were in the good-to-excellent range, their classification reliability was unacceptable. Commercial AI is still at a level that makes it challenging to replace manual landmarking by human experts. Thus, a human examiner's landmark position review is mandatory when using commercial AIs.

Commercial artificial intelligence lateral cephalometric analysis: part 2-effects of human examiners on artificial intelligence performance, a pilot study.

At the current technology level, a human examiner's review must be accompanied to compensate for the insufficient commercial artificial intelligence (AI) performance. This study aimed to investigate the effects of the human examiner's expertise on the efficacy of AI analysis, including time-saving and error reduction. Eighty-four pretreatment cephalograms were randomly selected for this study. First, human examiners (one beginner and two regular examiners) manually detected 15 cephalometric landmarks and measured the required time. Subsequently, commercial AI services automatically identified these landmarks. Finally, the human examiners reviewed the AI landmark determination and adjusted them as needed while measuring the time required for the review process. Then, the elapsed time was compared statistically. Systematic and random errors among examiners (human examiners, AI and their combinations) were assessed using the Bland-Altman analysis. Intraclass correlation coefficients were used to estimate the inter-examiner reliability. No clinically significant time difference was observed regardless of AI use. AI measurement error decreased substantially after the review of the human examiner. From the standpoint of the human examiner, beginners could obtain better results than manual landmarking. However, the AI review outcomes of the regular examiner were not as good as those of manual analysis, possibly due to AI-dependent landmark decisions. The reliability of AI analysis could also be improved by employing the human examiner's review. Although the time-saving effect was not evident, commercial AI cephalometric services are currently recommendable for beginners.

The Association between Korean Clinical Nurses' Workplace Bullying, Positive Psychological Capital, and Social Support on Burnout.

Recurring shortages of nursing peoplepower in recent Korean society have impacted nursing organizations with burnout accounting for a major part of nursing staff turnover. Thus, we studied the associations between workplace bullying, positive psychological capital, and social support and whether they predict nursing burnout. We used hierarchical regression analysis to observe changes in influencing factors by sequentially entering general traits, workplace bullying, positive psychological capital, and social support from 166 clinical nurses at two hospitals. The analysis showed that being female (ß = 0.18), working three shifts (ß = 0.40), workplace bullying (ß = 0.24), and positive psychological capital (ß = -0.28) were predictors of burnout (F = 11.25, p < 0.001), explaining 44.5% of the variance. An analysis of the correlations between burnout, workplace bullying, positive psychological capital, and social support revealed that workplace bullying was positively correlated with burnout (r = 0.36, p < 0.001), and positive psychological capital (r = -0.49, p < 0.001) and social support (r = -0.37, p < 0.001) were negatively correlated with burnout. Thus, the higher positive psychological capital within an organization, the lower the level of burnout, suggesting that organizations should consider education programs to promote positive psychological capital. In addition, healthy organizational culture should be promoted by monitoring workplace bullying.