Insights into the APAC PACS market and AI developments in medical imaging

According to a report by Meticulous Research, the global Vendor Neutral Archive (VNA) market & Picture Archiving and Communication System (PACS) market is expected to grow at a CAGR of 7.2% from 2018 to 2023 to reach $3.87B by 2023. In China, the demand of PACS & VNA is growing, as 70% of the medical procedures need images, including CT, X-ray, and magnetic resonance imaging.

Esteban Rubens, Global Enterprise Imaging Principal, Pure Storage, shared his insights on the PACS market in the Asia Pacific market and thoughts on the developments of AI in medical imaging.

Trends and developments in the Asia Pacific PACS Market

“I think cloud plays a large part of what people are looking at in Asia Pacific, even more so than in the US and Europe. In the US, there’s some resistance to moving the traditional areas of healthcare imaging (PACS and VNA) to the public cloud. In Asia, there’s much more interest in leveraging the cloud for imaging and I think the region is more advanced along that journey,” said Rubens.

In China, there is massive investment from both the government and the companies specifically around imaging but the focus is more on access to healthcare and bringing healthcare to people who are underserved. Outside of tier one or tier two cities in the country, the healthcare experience can be complicated with long waits and queues. For the case of Japan, there is access to good healthcare but the emphasis is more on early detection and tackling issues related to a rapidly aging population. 

“There are a lot of new players in imaging, but they seem to be only capturing the low end of the market. The high end is dominated by the big players from the US and Europe. Looking at the region, GE Healthcare made huge investments in China for decades and are now reaping the rewards. In Japan, Fujifilm dominates their home market. Outside of those two companies, its fragmented elsewhere in Asia and we expect some consolidation to take place in the coming years,” he added.

Helping healthcare organisations manage their increasing appetite for data

Due to the large volumes of imaging data as well as storage and accessibility concerns, Rubens believes that the best approach for healthcare organisations is a hybrid one, keeping some images on-premise and others in the cloud. 

“We have solutions that essentially run in the cloud such as Cloud Block Store, which allows the customer to duplicate the experience they have with a Pure FlashArray on-premise in the cloud regardless of their cloud provider. There is also ObjectEngine, the result of a recent acquisition we made, that allows people to put their backup copies in the cloud with up to 90% data reduction, which is unheard of. This has made it really viable for organisations to do backup and restore to the cloud.”

Backup for healthcare organisations is important because of regulatory requirements. Due to the on-going threats from hackers, they would also want to keep as many backups as possible. However, this can get expensive especially if they have to backup petabytes of images. ObjectEngine integrates the client’s backup application and the backup is sent to the cloud where data reduction is applied, which results in lower costs and also the flexibility to restore that data.

“Backups are only as useful as the restores, which is where the rubber meets the road. We believe that focusing on the rapid restoration of data from backups is crucial, especially in healthcare. If you get hit by ransomware, for example, you can’t afford to wait days or weeks to restore your data and applications, especially on the clinical side,” shared Rubens.

AI developments in medical imaging

According to Rubens, AI is really good at is detection and segmentation, especially of images and thus a natural fit for medical imaging. In addition, there is a worldwide shortage of radiologists – there is an exponentially growing set of images but not enough trained people to interpret them.

“The only way to bridge that gap is technology and AI is such a perfect fit. We’ve trained computers to do triage, take accurate measurements and identify lesions. There are very specific applications for each algorithm and it’s a big job to decide which images to send to which algorithm. So the challenge is now bringing all of that from the research side to the clinical side,” he explained.

Leveraging data and reaping the benefits of automation 

Regardless of their size, healthcare organisations are holding data and that is very valuable both for patients in terms of improved care as well as for the organisations themselves in terms of efficiency and cost savings. However, approaching data may be a daunting task, especially for smaller healthcare organisations or hospitals.

Rubens’ advice for them was to start small: “You don’t have to be a huge research hospital with a medical school attached, or an engineering school, to do this kind of research. It’s all very democratic, you can download these models from the internet, go to GitHub, open source stuff and then you do tutorials on Coursera so you can start training models with your own data.”

“Every organisation has someone who is interested in AI, so it’s important to support those people and recognise that what they want to do is important to the organisation’s development and goals,” he concluded.

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