Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?

OBJECTIVE: To test medical students' perceptions of the impact of artificial intelligence (AI) on radiology and the influence of these perceptions on their choice of radiology as a lifetime career. METHODS: A cross-sectional multicenter survey of medical students in Saudi Arabia was conducted in April 2019. RESULTS: Of the 476 respondents, 34 considered radiology their first specialty choice, 26 considered it their second choice, and 65 considered it their third choice. Only 31% believed that AI would replace radiologists in their lifetime, while 44.8% believed that AI would minimize the number of radiologists needed in the future. Approximately 50% believed they had a good understanding of AI; however, when knowledge of AI was tested using five questions, on average, only 22% of the questions were answered correctly. Among the respondents who ranked radiology as their first choice, 58.8% were anxious about the uncertain impact of AI on radiology. The number of respondents who ranked radiology as one of their top three choices increased by 14 when AI was not a consideration. Radiology conferences and the opinions of radiologists had the most influence on the respondents' preferences for radiology. CONCLUSION: The worry that AI might displace radiologists in the future had a negative influence on medical students' consideration of radiology as a career. Academic radiologists are encouraged to educate their students about AI and its potential impact when students are considering radiology as a lifetime career choice. ADVANCES IN KNOWLEDGE: Rapid advances of AI in radiology will certainly impact the specialty, the concern of AI impact on radiology had negative influence in our participants and investing in AI education and is highly recommended.

Burnout Phenomenon and Its Predictors in Radiology Residents.

RATIONALE AND OBJECTIVES: The purpose of this study is to evaluate the prevalence of burnout and its associated risk factors in radiology residents in Saudi Arabia. MATERIALS AND METHODS: This cross-sectional study was conducted in February 2019, and all radiology residents in Riyadh, Saudi Arabia, were invited to complete a survey that contained a validated measure of burnout (Maslach Burnout Inventory-Human Services Survey) alongside possible predictors of burnout. RESULTS: A total of 108 responses were received, for a response rate of 49.7%. High overall burnout was reported by 24.1% of respondents, high emotional exhaustion (EE) by 56.5%, high depersonalization by 31.5%, and low sense of personal accomplishment (PA) by 64.8%. The significant predictors of burnout included satisfaction with work/life balance (OR = 0.35, 95% CI = 0.03 to 0.43, p = 0.002) and exercising (OR = 0.31, 95% CI = 0.1 to 1, p = 0.07). Married residents were more prone to have a low sense of PA in addition to dissatisfied residents with hospital staff appreciation (OR = 4.8, 95% CI = 1.48 to 15.5, p = 0.01) and (OR = 0.59, 95% CI = 0.37 to 0.94, p = 0.03), respectively. CONCLUSION: One-fourth of the radiology residents studied showed high rates of burnout, and more than half the residents reported high rates of EE. The residents scored very poorly in the sense of PA. The radiology residents who were satisfied with their work/life balance had lower burnout rates, in addition to lower EE and a higher sense of PA.