Burnout crisis in Chinese radiology: will artificial intelligence help?

To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China. This study employed a cross-sectional research design. From February to July 2024, an online survey was conducted among radiologists and technicians at 68 public hospitals in China. The survey utilized general information questionnaires, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) scale, and a custom-designed AI usage questionnaire. This study analyzed the correlation between AI software usage and occupational burnout, and general information was included as a control variable in a multiple linear regression analysis. The analysis of survey data from 522 radiology staff revealed that 389 (74.5%) had used AI and that 252 (48.3%) had used it for more than 12 months. Only 133 (25.5%) had not yet adopted AI. Among the respondents, radiologists had a higher AI usage rate (82.0%) than technicians (only 59.9%). Furthermore, 344 (65.9%) of the respondents exhibited signs of burnout. The duration of AI software usage was significantly negatively correlated with overall burnout, yielding a Pearson correlation coefficient of -0.112 (p < 0.05). Multiple stepwise regression analysis revealed that salary satisfaction, night shifts, duration of AI usage, weekly working hours, having children, and professional rank were the main factors influencing occupational burnout (all p < 0.05). AI has the potential to significantly help mitigate occupational burnout among radiology staff. This study reveals the key role that AI plays in assisting radiology staff in their work. Questions Although we are aware that radiology staff burnout is intensifying, there is no quantitative research assessing whether artificial intelligence software can mitigate this occupational burnout. Findings The longer staff use deep learning-based artificial intelligence imaging software, the less severe their occupational burnout tends to be. This result is particularly evident among radiologists. Clinical relevance In China, radiologists and technicians experience high burnout rates. Even if there is an artificial intelligence usage controversy, encouraging the use of artificial intelligence software in radiology helps prevent and alleviate this occupational burnout.

Fang X ，Ma C ，Liu X ，Deng X ，Liao J ，Zhang T ... -  《-》

Artificial Intelligence and Radiologist Burnout.

Liu H ，Ding N ，Li X ，Chen Y ，Sun H ，Huang Y ，Liu C ，Ye P ，Jin Z ，Bao H ，Xue H ... -  《-》

Can digital leadership transform AI anxiety and attitude in nurses?

The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to transform the negative attitudes and anxiety that may occur in nurses. It was aimed to reorganize the professional transformation in this parallel by analyzing the effect of digital leadership perception, which is explained as how nurses approach digital technologies and innovations and their awareness of how and with which methods they can use these technologies on artificial intelligence anxiety and attitude in the nursing profession. The study was designed as descriptive, correlational, and cross-sectional. The research was conducted by reaching 439 nurses working in hospitals operating in three different regions of Turkey by simple random sampling method. In the first part of the data collection tool used in this study, digital leadership scale, artificial intelligence use anxiety, and artificial intelligence attitude scales were used, including questions determining the demographic information of nurses, their relationship with technology, artificial intelligence usage status and its importance in the profession. It was determined that 29.8% of the nurses had a good relationship with technology, 66.3% knew about using artificial intelligence in health, and 27.3% wanted it to be more involved in their lives. It was determined that nurses' perceptions of digital leadership were at a medium level of 46.9% and a high level of 41.7%, 82.7% had a positive attitude towards artificial intelligence, and 82.7% had low or medium level anxiety when their artificial intelligence anxiety status was examined. There was a significant and negative relationship between digital leadership and AI anxiety (r = -0.434; p < 0.01), a significant and positive relationship between digital leadership and AI attitude (r = 0.468; p < 0.01), and a significant and negative relationship between AI attitude and AI anxiety (r = -0.629; p < 0.01). Finally, it was determined that nurses' perception of digital leadership indirectly affected AI anxiety through AI attitude (β = -0.230, 95% CI [-0.298, -0.165]). It is suggested that the anxiety and attitude towards artificial intelligence can be transformed positively with the effect of digital leadership, and in this parallel, the digital leadership phenomenon should be evaluated as a practical implementation strategy in integrating artificial intelligence into the nursing profession. Our study showed that artificial intelligence attitude has a mediating role in the indirect effect of the perception of digital leadership in nursing on AI anxiety. It was determined that nurses' digital leadership perception, artificial intelligence anxiety, and artificial intelligence attitude differed significantly with demographic variables.

Tarsuslu S ，Agaoglu FO ，Bas M 《-》

A validity and reliability study of the artificial intelligence attitude scale (AIAS-4) and its relationship with social media addiction and eating behaviors in Turkish adults.

In recent years, there has been a rapid increase in the use of the internet and social media. Billions of people worldwide use social media and spend an average of 2.2 h a day on these platforms. At the same time, artificial intelligence (AI) applications have become widespread in many fields, such as health, education, and finance. While AI has the potential to monitor eating behaviors and provide personalized health support, excessive use of social media and AI can lead to negative effects. These include addiction and reduced quality of life. It is important to examine the attitude toward AI and its relationship with social media addiction, eating behavior, and life satisfaction. Research on the connection between AI attitudes and eating habits is lacking, which emphasizes the necessity of validating AIAS-4 in Turkish in order to ensure its efficacy in this context. The first stage of the study aimed to adapt Grassini's (2023) Artificial Intelligence Attitude Scale (AIAS-4) into Turkish and assess its validity and reliability. In the second stage, it was aimed to examine the relationship between artificial intelligence attitude and social media addiction, eating behavior, and life satisfaction. This study cross-sectional and methodological study was conducted in two stages in Türkiye. 172 adult individuals underwent a validity and reliability study in the first stage (43% of them were men and 57% were women), which involved adapting the AIAS-4 into Turkish. In the second stage, the relationships between artificial intelligence attitude, social media addiction, eating behavior, and life satisfaction of 510 individuals were evaluated with an average age of 24.88 ± 7.05 years (30.8% male, 69.2% female). Using the snowball sampling technique, the survey was carried out on adults by reaching out to staff and their families from both universities (Gazi University and Tokat Gaziosmanpaşa University) as well as students and their relatives. A face-to-face survey approach (delivered by an interviewer) was used for the study. In this study, the Social Media Addiction Scale-Adult Form(SMAS-AF) was used to assess social media addiction, the Scale of Effects of Social Media on Eating Behavior (SESMEB) was used to measure the impact of social media on eating behavior, the Contentment with Life Assessment Scale was used to evaluate life satisfaction, and the Eating Disorder Examination Questionnaire (EDE-Q total) was used to assess eating disorder symptoms. Pearson Correlation and Spearman Correlation according to normality and Linear regression analysis were used to analyse variables. AIAS-4 was a valid and reliable instrument in this study conducted in Türkiye (Cronbach's alpha = 0.90 and McDonald's omega = 0.89). Individuals spend an average of 3.7 ± 1.99 h per day on social media. All participants used WhatsApp, while 89.8% used Instagram. A negative correlation was found between AIAS and EDE-Q total, (r=-0.119 p < 0.05). BMI correlated positively with EDE-Q total (r = 0.391, p < 0.01). Higher AIAS scores were associated with increased time spent on social media (r = 0.129, p < 0.001). Conversely, higher AIAS scores were associated with lower EDE-Q total scores (r= -0.119, p < 0.001). SESMEB correlated positively with EDE-Q total (r = 0.169; p < 0.001). The model showed that BMI (β = 0.311; p < 0.001), AIAS (β =-0.157, p = 0.005), SMAS-AF (β = 0.036; p = 0.002) and SESMEB (β = 0.022; p = 0.038) affected EDE-Q total (p < 0.001 R2 = 0.198). This study revealed that the Artificial Intelligence Attitude Scale (AIAS) is valid and reliable for Turkish adults. The results show that BMI, social media addiction have positive, and AI attitude has negative impact on eating behaviors. These findings emphasize the importance of multidisciplinary approaches and awareness programs in the prevention and management of eating disorders.

Arslan N ，Esin K ，Ayyıldız F 《-》

The AMBER care bundle for hospital inpatients with uncertain recovery nearing the end of life: the ImproveCare feasibility cluster RCT.

Koffman J ，Yorganci E ，Murtagh F ，Yi D ，Gao W ，Barclay S ，Pickles A ，Higginson I ，Johnson H ，Wilson R ，Bailey S ，Ewart C ，Evans C ... -  《-》