DMC Radiology Reporting co-founder and clinical director Professor Sujal Desai, career–long cardiac and thoracic radiologist, talks about the role of AI and the upcoming RCR annual conference.
There is widespread recognition of shortfalls in the radiology workforce not only in the UK but also worldwide. According to the RCRi, the UK has a 29% shortfall of clinical radiologists, which is expected to rise to 40% in five years at the current trajectory without further intervention. The NHS Long Term Workforce Planii states that, by 2037, unless more is done, two-thirds of over 65-year-olds will have multiple health conditions and that, inaction in the face of demographic change is forecast to leave the overall NHS workforce shortfall of between 260,000 and 360,000 FTEs by 2036-37.
“It is a fact that the numbers of radiologists in the UK has never kept pace with the growth in other specialities.”
Not surprisingly, when considering the numbers of radiologists per 100,000 population, the UK resides perilously close to the bottom of the European league tables!
Radiology, as a speciality, continues to grow in complexity. The days of simply reviewing x-ray examinations have long passed. The need for ever-greater specialisation in radiology is widely acknowledged. One area where this will certainly be true is in the field of lung cancer screening – as the NHS/government programme for ‘Targeted Lung Health Checks’ is rolled-out nationally, the need for highly-skilled and trained thoracic radiologists will grow.
The role of AI in Radiology
Much is spoken about the role of Artificial Intelligence (or AI) in medicine in general but in radiology specifically. While AI undoubtedly will have a role to play, the notion that AI will solve the workforce challenges is perhaps overoptimistic! More likely than not, the radiologist of the future will be the radiologist working with AI than the radiologist replaced by AI. There is little doubt that AI has the capacity to make many tasks easier which, ultimately, will have a bearing on improved patient care.
In my field of thoracic radiology, monitoring diseases frequently entails quantifying or assessing the extent of disease, a task that is inevitably prone to human error and between-observer variation. By contrast, AI does not suffer from such issues and can more reliably determine patterns of disease and changes in severity which is of particular value when monitoring.
“AI algorithms are already showing considerable promise in quickly and accurately analysing medical images — from plain x-ray studies to more complex CT and MRI examinations — detecting abnormalities, highlighting potential areas of concern, and iding radiologists in making diagnoses. This capability extends across all domains of radiology.”
As with any new technology a degree of healthy cynicism must be maintained. The volume of data potentially generated by AI algorithm is seemingly voluminous, much of which may have no clinical / prognostic meaning.
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