Imaging Pearls ❯ Deep Learning ❯ AI and Physician Burnout
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- “Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplacenefficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures,and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.”
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. - 1. The recent developments of AI technologies create significant potential for increasing efficiency in radiology, improving the diagnostic accuracy of radiologists using various imaging modalities, and most importantly, bettering overall patient care.
2. To encourage the use of AI in radiology, challenges of its use such as the unclear nature of AI algorithms, present data imbalances, and limitations in detecting specific diseases, must be further researched and addressed.
3. This review article highlights the need of understanding factors that affect AI model performance, such as type, location, size, artifacts, calcifications, and post-surgical changes, in order to efficiently and accurately diagnose conditions like intracranial hemorrhage, spinal fractures, and rib fractures in the context of emergency radiology.
Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.
Fathi M, Eshraghi R, Behzad S, et al..
Emerg Radiol. 2024 Dec;31(6):887-901. 
- Question Is the use of artificial intelligence (AI) in radiology practice associated with radiologist burnout?
Findings In this cross-sectional study, the use of AI was associated with burnout among radiologists, exhibiting a dose-response association. This association was particularly pronounced in radiologists with high workload and those with low AI acceptance.
Meaning These findings suggest the need for harmonious integration of AI tools with radiologists to effectively mitigate burnout in radiology practice.
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 - OBJECTIVE To estimate the association between AI use in radiology and radiologist burnout.
DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study conducted a questionnaire survey between May and October 2023, using the national quality control system of radiology in China. Participants included radiologists from 1143 hospitals. Radiologists reporting regular or consistent AI use were categorized as the AI group. Statistical analysis was performed from October 2023 to May 2024.
CONCLUSIONS AND RELEVANCE In this cross-sectional study of radiologist burnout, frequent AI use was associated with an increased risk of radiologist burnout, particularly among those with high workload or lower AI acceptance. Further longitudinal studies are needed to provide more evidence.
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 - “Burnout encompasses emotional exhaustion (EE), depersonalization (DP), and a diminished sense of personal accomplishment. Physician burnout has emerged as a global issue, primarily driven by work overload, conflicts between home and work life, and job dissatisfaction. A previous systematic review has linked physician burnout to career disengagement, high physician turnover, and reduced quality of patient care. Radiologists exhibit higher burnout rates compared with other medical specialists. In Germany, over 75% of radiologists have reported experiencing burnout, while approximately40%of their US counterparts report similar conditions. A recent analysis estimated that 83%of radiologists exhibit at least 1 symptom of burnout. Moreover, elevated burnout levels have been documented across various radiology subspecialties, including interventional, musculoskeletal, pediatric, and breast.”
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 - “To our knowledge, this study is the first to investigate the association between AI use and radiologist burnout using a large, nationwide cross-sectional sample. We found that AI use was associated with increased odds of burnout among radiologists, exhibiting a significant dose-response association. Moreover, joint exposure to AI use alongside either high workload or low AI acceptance was associated with an additional risk of burnout. These results underscore the need to reassess the role of AI technology in mitigating radiologist burnout. Balancing AI use with an appropriate radiology workforce and maintaining psychological acceptance of AI technology in clinical practice is essential.”
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 - ”Our results indicate that the association of AI use with burnout may be exacerbated by increasing workload. The integration of AI technology in hospitals could lead to higher consultation volumes and increase radiologists’ workload. Although data to substantiate this are limited, policymakers are considering increasing radiology capacity with AI to address rising care demands, particularly in tertiary hospitals. For radiologists, the inherently isolated and sedentary nature of their work contributes to higher burnout rates compared with other specialties. AI may further exacerbate these challenges by diminishing opportunities for peer collaboration and patient interaction, while fears of job displacement and uncertainties surrounding AI use heighten stress. Joint association analyses showed that the risk of burnout associated with AI use was particularly pronounced among radiologists with high workloads, highlighting the urgent need to explore effective coordination strategies between radiologists and AI.”
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 - “Radiologists, already in short supply, are overwhelmed by rapidly growing health care needs and medical imaging data. In China, the annual growth rate of medical imaging data are 7.5 times that of radiologists. Therefore, radiologists’ workload and burnout are receiving unprecedented attention. AI technology offers a potential solution to the shortage of radiologists. Policymakers and researchers are planning or have implemented AI strategies in radiology to address the supply demand imbalance while maintaining the same or a slowly growing radiology workforce. However, the excitement and expectations surrounding technological advances should not overshadow the challenges that remain before AI can be routinely applied in radiology practice. AI tools must provide clinical results that radiologists can understand and trust to truly reduce workload. Few AI technologies have been rigorously validated in randomized clinical trials.37 Furthermore, integrating AI tools into the radiology workflow should be a key research task.”
Artificial Intelligence and Radiologist Burnout
Hui Liu, et al.
JAMA Network Open. 2024;7(11):e2448714. doi:10.1001/jamanetworkopen.2024.48714 
- “There has been an increase in the number of imaging studies performed worldwide over the past 2 decades. There has been a simultaneous increase in the number of artificial intelligence (AI) models approved by the Food and Drug Administration (FDA) in the United States, with over 75%of these models approved for use in radiology. AI has been used to assist with interpretative tasks (detection, diagnosis, prognosis) and noninterpretive tasks (creating reports, protocols, contacting ordering clinicians, scheduling) in radiology. It is reasonable to assume that AI would be a useful adjunct for radiologists, increase radiologist efficiency, and decrease radiologist burnout. However, some reports, including the report by Liu et al, suggest that this may not be true.”
Artificial Intelligence Impact on Burnout in Radiologists— Alleviation or Exacerbation?
Farid Ghareh Mohammadi, PhD; Ronnie Sebro
JAMA Network Open. 2024;7(11):e2448720. - Burnout is a syndrome caused by unmanaged chronic workplace stress, characterized by 3 main dimensions: (1) energy depletion or exhaustion, (2) increased mental detachment or negativity toward one’s job, and (3) a sense of ineffectiveness and lack of achievement. The burnout rate in radiologists is high.
Artificial Intelligence Impact on Burnout in Radiologists— Alleviation or Exacerbation?
Farid Ghareh Mohammadi, PhD; Ronnie Sebro
JAMA Network Open. 2024;7(11):e2448720. - “To examine the association between AI use and burnout, the authors used propensity score–based multivariable logistic regression. They assessed workload (working hours on image interpretation, the amount of image interpretation, device type, role in the reporting workflow, and hospital level) and categorized radiologists into 3 groups based on their scores: low (0-2), medium (3), and high (4-5), which assumes a linear relationship between scores. Personal and professional characteristics were gathered through a self-designed questionnaire while researchers evaluated radiologists based on psychological factors using the Gallup Q12 Employee Engagement scale, measuring perceived control, spiritual rewards, work values, organizational support, and coworker support. The authors examined the association between AI use and burnout, adjusting for personal and professional characteristics, workload score, AI acceptance, and psychological factors. AI use was treated as both categorical and continuous to show the dose-response association.”
Artificial Intelligence Impact on Burnout in Radiologists— Alleviation or Exacerbation?
Farid Ghareh Mohammadi, PhD; Ronnie Sebro
JAMA Network Open. 2024;7(11):e2448720. - “The use of AI was associated with higher odds of burnout among radiologists. This association exhibited a dose-response association, meaning that as AI use increased, so did the likelihood of experiencing burnout. While there was an association between AI use and burnout, this was a crosssectional study and thus it is unclear the temporal order and cause and effect. One assumption could be that AI use causes burnout; however, an alternative and equally plausible assumption is that radiologists with burnout are more likely to use AI, perhaps to decrease burnout. The findings by Liu et al indicate that higher levels of AI use may be associated with an increase in burnout among radiologists.”
Artificial Intelligence Impact on Burnout in Radiologists— Alleviation or Exacerbation?
Farid Ghareh Mohammadi, PhD; Ronnie Sebro
JAMA Network Open. 2024;7(11):e2448720. - “It is unclear whether the AI models evaluated in this study performed both interpretive and noninterpretive tasks. Human-machine interaction (HMI) refers to how humans and machines interact and communicate with each other. This article highlights that the health care community has not yet learned how to effectively work with AI to harness the power of AI to reduce burnout. The nuances of HMI are still in its infancy, and until we are more effective at HMI, AI will not be a panacea to reduce radiologist burnout.”
Artificial Intelligence Impact on Burnout in Radiologists— Alleviation or Exacerbation?
Farid Ghareh Mohammadi, PhD; Ronnie Sebro
JAMA Network Open. 2024;7(11):e2448720.
