AI Impact on the Teleradiology Market: The Signify View
Published: March 25, 2020
Cranfield, UK, 25th March 2020 – Written by Alex Green – Earlier this week, Medica Group, the largest UK teleradiology reading service provider, announced a strategic relationship with Qure.ai, a supplier of Artificial Intelligence (AI) solutions for radiology, to develop AI tools for prioritisation and improved efficiency of radiology scan workload. Specifically, the agreement included:
- A plan to launch a decision support tool for CT head scan examinations that will flag potential urgent examinations. The tool will also highlight potentially critical findings to reporters, which can be integrated into diagnoses. It will be trialled and implemented to augment Medica’s urgent, out-of-hours NightHawk service.
- The second area is to co-develop an AI-based automated workflow improvement tool that aims to improve the efficiency of study allocation from NHS clients to Medica’s network in the UK.
The latest announcement follows several others that have been made by reading service providers, IT suppliers and AI algorithm developers in relation to teleradiology and its use of AI. This insight explores the potential that teleradiology has to offer AI algorithm developers and the impact AI will have on teleradiology.
Teleradiology Market Potential
This month will see Signify Research publish is 2020 global teleradiology market report. The report examines the penetration that teleradiology has made into the overall number of diagnostic imaging procedures performed globally and, specifically, in 20 core countries and sub-regions. It presents our estimates and forecasts for the market for teleradiology reading services (reading volumes, revenues and revenue per read), teleradiology IT and the competitive environment (from a reading service provider and IT vendor perspective) in each of the 20 countries and sub-regions mentioned above.
It is estimated that there were 4.7B diagnostic imaging scans performed globally in 2019 (including in house, using radiology groups and using teleradiology reading service providers), a number that is forecast to grow at approximately 3% per annum over the next five years. X-ray exams accounted for most of these scans in 2020, but others such as MRI and CT are forecast to grow at a faster rate over the coming years, as they have over the last decade.
Signify Research defines teleradiology as the electronic transmission of radiological patient images from a scanning organisation to a different reading organisation, for the purposes of diagnostic interpretation and reporting. In 2019 less than 2% of the 4.7B diagnostic imaging scans performed globally fell into this category.
However, several drivers will contribute to this penetration rate increasing significantly over the next five years. These include:
- Shortages of radiologists in certain countries/regions
- Increased demand for more specialised modality reads such as CT and MRI, that require radiologists with specific skills
- Demand for out of hours reporting, particularly in time-critical applications, e.g. neurology
- Longer read times for more specialised modalities (e.g. CT v X-ray)
- Increased use of cloud-based technology making implementing IT for teleradiology less complex
- Changes in legislation that support reading services being provided out of country and by third parties
- Increasing numbers of imaging IT vendors offering workflow tools that are designed specifically for teleradiology applications
One final driver, not mentioned above, is that of technology advances, in particular those relating to AI.
How Will AI be Used by Teleradiology Reading Service Providers?
Three of the most important factors that heavily influence the success of teleradiology reading service providers are:
- The speed that radiologists working for teleradiology service providers perform and report on their reads.
- The accuracy of the reports produced by radiologists.
- The workflow and decision support processes that service providers put in place to ensure urgent reads are prioritised and reported on quickly.
Over time, AI can be used to support and improve all three.
The Medica/Qure.ai announcement this week is a good example of how AI is being used to support the third point – improvements in workflow and decision support. Both of the main areas of focus highlighted in the announcement were aimed at improving workflows and decision support. This is forecast to be a key area for AI in teleradiology, particularly in relation to emergency X-ray and emergency CT scans, which can account for most out of hours teleradiology service provider workload.
Other teleradiology reading service providers have also been exploring how AI can be used to improve workflows and decision support. For example, US teleradiology service provider, vRad, has also been working with Qure.ai on worklist prioritisation, specifically in relation to intercranial haemorrhage on head CT scans. Other examples include Real Time Medical of Canada collaborating with Google Cloud to develop AI-assisted workload balancing tools; I-Med Radiology Network of Australia implementing AI tools for worklist triage in the areas of brain haemorrhage, pulmonary embolism and C-spine fracture; and Global Diagnostics Group, again of Australia, partnering with Aidoc, to implement AI-based workflow solutions supporting care management pathway development. (Note these are just examples and not a comprehensive list).
The first two bullets (read speed and accuracy) will become increasingly important as the types of diagnostic imaging scans being performed become increasingly focused on more complex and time-consuming procedures. For example, 11.5% of diagnostic imaging procedures performed globally in 2019 were CT scans, this ratio has been increasing steadily over the last decade and is projected to reach 14% of all diagnostic imaging scans in 2024. Conversely, X-rays accounted for 61% of all scans in 2019, a figure that is projected to fall to approximately 55% in 2024.
Whilst X-rays accounted for most of the scans performed, it is estimated that they accounted for less than 20% of total radiologist reading time, due to faster reading times per scan. The net effect of the changing complexion of scan types forecast over the next five years is, not only are diagnostic procedures increasing, but procedures that take a disproportionately longer time to report are growing fastest, increasing the demand for radiologist resource.
AI offers a huge competitive advantage for teleradiology reading service providers that can reduce these read times, whilst maintaining (or even better, improving) accuracy.
Again, several AI algorithm developers have been working with teleradiology service providers to put in place tools that will support increasing read speed and accuracy. For example, US teleradiology service provider, USARAD, and Siemens collaborated on the lung nodule detection part of Siemens’ AI-RAD Companion Chest CT solution. In September 2018, US radiology service provider, SimonMed Imaging, selected Riverain Technologies’ AI application (ClearRead CT) to improve lung cancer nodule detection accuracy and nodule search efficiency. Additionally, in July 2019, SimonMed also implemented an AI-based 3D mammography breast cancer detection solution developed by iCAD. (Again, these are just examples and not a comprehensive list).
Time to Impact
Whilst there has been plenty of activity in relation to teleradiology and AI, the reality is that it will take a considerable amount of time to have a significant impact on the market and the three main factors of success highlighted above (speed, accuracy and workflow/decision support).
The slow, expensive and relatively opaque regulatory approval processes, shortage of clinical validation studies on the efficacy and efficiency of solutions in clinical practice, shortage of annotated datasets to train algorithms, and the pace of IT integration are all forecast to mean that, outside of solutions for workflow optimisation, it will be beyond the five year forecast period of our upcoming report before we see AI having a major impact on the teleradiology market. This should not be interpreted as AI not being important for teleradiology, it is, and those reading service providers and AI algorithm developers that are at the forefront as these barriers are conquered will put themselves in an advantageous competitive position; however, it is a long play and one that still has many hurdles to overcome.
About Signify Research’s Teleradiology Market Report
Signify Research’s upcoming teleradiology market report provides a global analysis of the teleradiology market, both in terms of reading services and the teleradiology IT market. The report includes an analysis of the competitive environment for both reading service providers and teleradiology IT vendors, with more than 35 companies profiled. The analysis of the teleradiology IT market includes estimates and forecasts at a country level for demand for imaging IT and workflow IT used in teleradiology, as well as discussion on the role that AI will play. Please contact us if you are interested in purchasing the report.Share on LinkedIn