A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

PURPOSE: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing. METHOD: An online questionnaire was created using the SurveyMonkey platform targeting radiologists, students and surgeons throughout the German speaking part of Switzerland. A total of 170 people participated in the survey (59 radiologists, 56 surgeons and 55 students). Statistical analysis was carried out using the Kruskal-Wallis test with Dunn's multiple comparison post-hoc tests. RESULTS: While the majority of participants agreed that AI should be included as a support system in radiology (Likert scale 0-10: Median value 8), surgeons were less supportive than radiologists (p = 0.001). Students saw a potential threat of AI as more likely than radiologists did (p = 0.041). When asked whether they were concerned about "turf losses" from radiology to other disciplines, radiologists were much more likely to agree than students (p < 0.001). Of the students that do not intend to specialize in radiology, 26 % stated that AI was one of the reasons. Surgeons advocate the use of teleradiology. CONCLUSIONS: With regard to AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, whereas medical students and surgeons tend to be more skeptical towards this technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are rather afraid of turf losses.

Intermittent child employment and its implications for estimates of child labour.

Using longitudinal data from urban Brazil, the authors track the employment patterns of thousands of children aged 10-16 during four months of their lives in the 1980s and 1990s. The proportion of children who work at some point during a four-month period is substantially higher than the fraction observed working in any single month. The authors calculate an intermittency multiplier to summarize the difference between employment rates in one reference week vs. four reference weeks over a four-month period. They conclude that intermittent employment is a crucial characteristic of child labour which must be recognized to capture levels of child employment adequately and identify child workers.