The impacts of artificial intelligence on the workload of diagnostic radiology services: A rapid review and stakeholder contextualisation

Background: Advancements in imaging technology, alongside increasing longevity and co-morbidities, have led to heightened demand for diagnostic radiology services. However, there is a shortfall in radiology and radiography staff to acquire, read and report on such imaging examinations. Artificial intelligence (AI) has been identified, notably by AI developers, as a potential solution to impact positively the workload of radiology services for diagnostics to address this staffing shortfall. Methods: A rapid review complemented with data from interviews with UK radiology service stakeholders was undertaken. ArXiv, Cochrane Library, Embase, Medline and Scopus databases were searched for publications in English published between 2007 and 2022. Following screening 110 full texts were included. Interviews with 15 radiology service managers, clinicians and academics were carried out between May and September 2022. Results: Most literature was published in 2021 and 2022 with a distinct focus on AI for diagnostics of lung and chest disease (n = 25) notably COVID-19 and respiratory system cancers, closely followed by AI for breast screening (n = 23). AI contribution to streamline the workload of radiology services was categorised as autonomous, augmentative and assistive contributions. However, percentage estimates, of workload reduction, varied considerably with the most significant reduction identified in national screening programmes. AI was also recognised as aiding radiology services through providing second opinion, assisting in prioritisation of images for reading and improved quantification in diagnostics. Stakeholders saw AI as having the potential to remove some of the laborious work and contribute service resilience. Conclusions: This review has shown there is limited data on real-world experiences from radiology services for the implementation of AI in clinical production. Autonomous, augmentative and assistive AI can, as noted in the article, decrease workload and aid reading and reporting, however the governance surrounding these advancements lags.