Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Abstract

Objective To determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists. Methods This study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital. Results In the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25–34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19–34.21) in the non-academic general teaching hospital setting. Conclusions Recently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.

Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology

“…The actual contributions of AI to the workload of diagnostic radiologists were assessed in a recent analysis based on large number of published studies. It was concluded that although there was often added value to patient care, workload was decreased in only 4% but increased in 48% and remained unchanged in 46% institutions [ 2 ]. The results of the present survey are somewhat more optimistic since almost 23% of respondents experienced a reduction of their workload when using algorithms for diagnostic assistance in clinical practice, whereas almost 70% did not.…”

“…In an opinion survey conducted in 2018 among the members of the European Society of Radiology (ESR), many respondents had expectations that algorithms based on artificial intelligence (AI) and particularly machine learning could reduce radiologists’ workload [ 1 ]. Although a growing number of AI-based algorithms has become available for many radiological use case scenarios, most published studies indicate that only very few of these tools are helpful for reducing radiologists’ workload, whereas the majority rather result in an increased or unchanged workload [ 2 ]. Furthermore, in a recent analysis of the literature it was found that the available scientific evidence of the clinical efficacy of 100 commercially available CE-marked products was quite limited, leading to the conclusion that AI in radiology was still in its infancy [ 3 ].…”

A methodology for selection and quality control of the radiological computer vision deployment at the megalopolis scale

“…Multiple publications of American College of Radiology and the British Institute of Radiology describe the possible benefits of AI implementation in a routine radiological practice (5,6). In a 2019, an AIdependent decrease in the radiologists' workload was noted only in 5% of publications; in the rest of the assessments, radiologist efforts increased due to the need to learn a new software and increase in the reporting time due to reading the additional results from AI (7). The same time the effective implementation of AI in medical facilities is associated with technical difficulties and reliable encryption of the received data when transmitting them outside the clinic (7), as well as lack of funds, regulatory policies, and support systems (8).…”

“…In a 2019, an AIdependent decrease in the radiologists' workload was noted only in 5% of publications; in the rest of the assessments, radiologist efforts increased due to the need to learn a new software and increase in the reporting time due to reading the additional results from AI (7). The same time the effective implementation of AI in medical facilities is associated with technical difficulties and reliable encryption of the received data when transmitting them outside the clinic (7), as well as lack of funds, regulatory policies, and support systems (8). On the other hand, a cornerstone of the AI implementation should be scientific and clinical validity, relevance to the intended purpose, and user-friendliness, which can reduce labor costs of the radiologists and increase the efficacy of radiological reports, i.e.…”

On-call abdominal ultrasonography: the rate of negative examinations and incidentalomas in a European tertiary care center

“…As a result of the growth in imaging utilization, the workload per individual radiologist has increased considerably over the past decades, and this trend is expected to continue [4,5]. Previous research in a large general hospital in Western Europe has shown that the overall workload during oncall hours has quadrupled in the past 15 years, particularly due to the growth in the number of CT studies (brain CT, remotely followed by body CT) [4].…”