Due to this covid-19 outbreak, the world has seen the potential of teleradiology globally. Many researches have been carried out to examine the penetration of teleradiology for image processing. Most of the diagnostic centers which always operated offline, have also got online in these trying times. This is beneficial in multiple ways. Diagnosis can be provided to the patients 24×7 and they can be treated quickly in case of emergency as the radiology results can be provided quickly to them, the diagnosis process can be executed faster and thereby the treatment. Secondly, it becomes important to keep social distance and allow the radiologist to diagnose from the safe environment of his own home or his personal diagnostic center, rather than stepping out to the clinic to read the reports.
To meet this increasing demand of teleradiology in these trying times, AI played a very vital role along with the medical software systems. Despite of IT being the foundation and the most important tool in carrying the radiology scans and virtually carrying it to the radiologist and bringing back the reports in the virtual format back to the diagnostic center or the clinic and thereby delivering it to the patient. The entire process of encryption and decryption becomes important along with encoding and decoding process at both the ends. AI goes a step further in helping automating the diagnosis scan and reading the scan like a robot. There is a lot which has been researched in the domain of AI for teleradiology but will still take a handful of years for the same to be executed and deployed all around the world. The day is not far when you will have an AI Robot reading the reports for you. To make it more accurate and precise, the final verdict of the radiologist is important but AI will still be important in increasing the speed of diagnosis.
Many researches have been carried out for image processing globally to present estimates and forecasts for teleradiology reading services. There were about 4.7B diagnostic imaging scans performed globally in 2019 which have been increasing by approx 3% year on year over last 5 years but that seems to have been doubled and become 6% this year. Majority of the scans in 2020 were x-ray scans followed by MRI and CT scan which also seemed to have increased in the recent years. Clinics and diagnostic centers have started developing higher amount of trust in teleradiology as it becomes a blessing in disguise for clinics and hospitals in remote locations where radiology experts are not easily available in the vicinity.
Teleradiology Market Potential
There is a great market potential when it comes to teleradiology as more and more clinics would appreciate this method of disguise and for the clinics in remote area this seems to a service which they had been deprived of for long. While this technology becomes readily available to more hospitals and more providers for the same, it will gradually reduce the rate per scan which will be another reason why higher number of diagnostics centers will prefer using teleradiology. With the advent of AI the readings become faster and researches say that they accounted for less than 20% of total radiologist reading time, which is a great savings on the cost and also time per scan. The net effect of this changing mode of diagnosis and complexion of scans over next five years will be noteworthy. AI not only improves upon the diagnosis procedures but also provides faster output for scans that need disproportionately longer time to report. This is the major reason for increase in 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 improving accuracy. There are several AI algorithms developed by IT organizations which can contribute greatly in this unique development which will change the face of the industry.
How will AI be used by Teleradiology Reading Service Providers in coming years to come?
Three most important factors that will heavily contribute in influencing the success of radiology are:
a.) The speed for radiology scans that radiologists working for teleradiology will provide and jot down their observations.
b.) The accuracy of the reports produced by radiologists.
c.) The workflow and decision making processes that service providers put in place to make sure urgent reads are prioritized and served in real time.
Over time, AI can be used to support and improve all these three important factors. Whilst there has been plenty of activities been undertaken in association to teleradiology and AI, the reality is that it will still take a considerable amount of time to have a significant visible impact on the market and improve upon the speed, accuracy and workflow/decision support for teleradiology.
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