Workforce Crisis in Radiology in the UK and the Strategies to Deal With It: Is Artificial Intelligence the Saviour?

Abstract

Radiology has seen rapid growth over the last few decades. Technological advances in equipment and computing have resulted in an explosion of new modalities and applications. However, this rapid expansion of capability and capacity has not been matched by a parallel growth in the number of radiologists. This has resulted in global shortages in the workforce, with the UK being one of the most affected countries. The UK National Health Service has been employing several conventional strategies to deal with the workforce situation with mixed success. The emergence of artificial intelligence (AI) tools that have the potential to increase efficiency and efficacy at various stages in radiology has made it possible for radiology departments to use new strategies and workflows that can offset workforce shortages to some extent. This review article discusses the current and projected radiology workforce situation in the UK and the various strategies to deal with it, including applications of AI in radiology. We highlight the benefits of AI tools in improving efficiency and patient safety. AI has a role along the patient's entire journey from the clinician requesting the appropriate radiological investigation, safe image acquisition, alerting the radiologists and clinicians about critical and life-threatening situations, cancer screening follow up, to generating meaningful radiology reports more efficiently. It has great potential in easing the workforce crisis and needs rapid adoption by radiology departments.

Keywords: ai; artificial intelligence; nhs; radiology workforce; uk radiology.

Figure 1. Estimated supply and demand of whole-time equivalent consultant radiologists in the UK.

Source: Royal College of Radiologists. Clinical Radiology UK Workforce Census 2019 report [1] (Reproduced with permission).

Figure 2. Outsourcing and insourcing.

Source: National Imaging Data Collection 2017/18, NHS Improvement [2].

Figure 3. Number of CT scanners in the UK.

Source: Lara et al. [10] (This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution, and reproduction in any medium).

Figure 4. Suggested algorithm for following up patients with severe COVID-19 pneumonia.

Source: George et al. [12] (Reproduced with permission).

Figure 5. Venn diagram representation of convolutional neural networks in the artificial intelligence hierarchic terminology.

Source: Soffer et al. [19] (This article is available under the Creative Commons CC-BY-NC license and permits non-commercial use, distribution, and reproduction in any medium).