Deep learning in medical imaging: the impact of cloud GPU scalability on diagnostics

The medical industry is on the verge of a technological revolution, with AI taking center stage and reshaping how medical professionals identify and handle diseases.

And no, it’s not a plot of a Terminator movie – the use of AI and its deep learning features not only cuts the time for processing piles of data, but it actively assists medical staff in running diagnostics way faster than before.

As I imagine you might be, I was initially skeptical about this. That’s why my team of Cybernews experts and I have researched the topic since the first AI breakthroughs in the industry, and now we’re ready to round up our findings in this guide. Let’s see how deep learning, and cloud GPUs like LiquidWeb, in particular, can change the way we diagnose and treat diseases.

Deep learning revolution in medical imaging: how it all started

Deep learning has been at the forefront of AI development in the medical industry for a while now, and it’s already used for processing large amounts of data in remarkably short periods. However, medical imaging, in particular, is an area that benefits from deep learning the most.

Back in the day (which was not so long ago), medical imaging was based solely on X-rays, CT scans, and MRIs, which collectively painted an accurate picture of the body’s internal functions. In turn, this helped medical experts diagnose, track, and potentially cure illnesses based on their interpretation of the data.

Now comes the part that I feared at first – the use of deep learning can speed up this rather long process and find medical implications that even doctors may have missed in a heartbeat. If it sounds like a frighteningly fast development of AI in such a serious field, you’re not alone.

However, we can’t argue with the results here, as these algorithms are capable of comparing thousands, if not hundreds of thousands of scans, images, and even mammograms to pinpoint abnormalities in a matter of seconds.

However, I don’t think this should be viewed as something negative – it’s quite the opposite. Doctors will soon be able to completely lean on these algorithms to find medical details and disease implications that they might have missed, and they will be able to completely trust those findings.

Of course, for this to work, there are certain factors in play. Medical personnel are still the key, as you need educated professionals to oversee the potential findings of deep learning algorithms. Well-trained deep learning systems are nearly just as important, and for that, cloud GPUs and servers provided by services like LiquidWeb are crucial.

How GPU cloud solutions impact the scalability of medical diagnostics

As you can probably guess, these forms of medical diagnostics require massive computational power. After all, it’s not like scrolling through your image library on an everyday smartphone. These models need to be efficient enough to go through gigabytes of data, and that’s just for a single MRI study.

Now, imagine how much computational power is needed for the model to review thousands of studies, compare them, and pinpoint medical implications that even doctors might have missed.

The only option capable of supporting synchronous advanced deep learning models is cloud GPU solutions. They’re used for training AI models, and they could have dozens of powerful cores that can run such calculations. Their case in medical deep learning applications is even stronger when you consider the time difference.

For instance, imagine this scenario – an average, physical CPU could take days to process huge amounts of data while also supporting the deep learning model. In the case of GPU-accelerated systems, like those provided by companies like LiquidWeb, it could take just a couple of hours.

Challenges and limits of the infrastructure

Despite the clear benefits of using deep learning models and GPU-accelerated systems for rapid diagnostics, their application in the medical field has been limited. It’s not just a matter of the medical personnel not trusting the AI process (though I’ve got to admit it’s a part of the reason), but it’s also about scalability.

Even if a medical institution were to invest in an on-premise GPU infrastructure, it would still limit the possibilities. Medical facilities aren’t exactly packed with free space that could be used for servers and processors to provide enough power capable of running advanced deep learning models.

That’s where cloud GPU solutions come into play. Here’s what they bring to the table:

• No need for physical infrastructure. Cloud solutions are perfect for medical institutions since they don’t require the facilities themselves to clear up any space. Instead, they work on a rent-based principle via the cloud.
• Cost-efficiency. To create the necessary power output for running deep learning models, medical institutions would need to spend thousands of dollars on each CPU, and the expense of building an entire network of these with dedicated servers would be sky-high. Instead, cloud-based solutions bring all that power at a fraction of the cost.
• No demand for upkeep. Without an on-premise system, medical facilities don’t need to hire IT specialists for maintenance. Plus, there’s no need to switch from dated processors to new ones capable of supporting the latest models. Cloud service providers do it instead.
• Vast power output. Finally, the power output differences between on-premise and cloud solutions are so vast that they’re not even comparable. Even if a hospital used the latest generation processors, cloud service providers like LiquidWeb would do the same job with the latest NVIDIA GPUs and dedicated cloud hosting servers to bump up computational speed.

The impact of deep learning diagnostics in practice

While the use of deep learning models for medical purposes sounds impressive, I wanted to see the difference it makes firsthand. As it turns out, GPU-accelerated cloud infrastructures have already been put to good use in several areas, starting with radiology.

Deep learning models can improve the detection rates of subtle abnormalities that even doctors might have missed. For instance, they could point to lung nodules at an early stage, and luckily, it can be done quickly enough to avoid lung cancer fatalities. Again, this is the beginning of applying this technology, so its limits are still unknown.

What is clear, however, is that these systems can help radiologists point their attention to where it’s needed the most. They’re capable of cutting hundreds of hours of examining images and MRI studies into minutes. They highlight potential red flags along the way, so radiologists can just focus on those cases and apply their expertise to diagnose or dismiss illnesses.

Contrary to what most believe, deep learning models are unlikely to replace medical professionals and their work. They lack the human judgment and firsthand expertise of radiologists, but that’s the whole point – they work as a perfect partner.

Doctors of the future might be able to completely lean on these models for image and study scanning and devote their expertise and experience to examining the ones flagged by the system.

It’s the same case with ophthalmology, where this technology might be able to scan for diabetic conditions and help specialized personnel act faster. In cardiology, deep learning analysis can be used for echocardiograms, MRIs, and other studies that point to issues with cardiac functions. Since cardiac imaging comes in three dimensions, GPU-accelerated systems can act and scan much faster than any human ever could.

The role of cloud service providers in powering medical innovation

Based on everything I’ve discussed above, it’s clear that the role of cloud service providers is crucial here. Companies like LiquidWeb have made a name for themselves by offering GPU-accelerated hosting solutions and NVIDIA chips that are unmatched in terms of computational prowess.

Their pre-configured servers are even capable of running multiple GPUs, depending on the system. In other words, they offer the flexibility that institutions need in scaling medical imaging.

Of course, certain limits come with this approach, and the main one is downtime. That’s why companies like LiquidWeb guarantee 24/7 uptime of their servers, which is crucial for maintaining the medical imaging flow. I probably don’t have to tell you that it could mean a life-saving difference for patients.

On the other hand, dedicated IP addresses and protection from DDoS attacks also come in as high priorities. Medical institutions need to ensure the safety of the patients’ information, which means that choosing the right provider is the number one priority.

Technical considerations and GPU examples

Besides the choice of a service provider, the selection of a GPU system is also crucial for medical companies to fully utilize these systems. Deep learning models need to be properly trained to be able to take on such advanced tasks.

That means they need to be capable of handling large batches of files with varying sizes, and that depends on GPU memory. It’s also one of the reasons why I’ve highlighted the use of servers with multiple GPUs.

Training the model is way easier when the tasks are distributed through several GPUs, and frameworks like PyTorch and TensorFlow give medical institutions the needed tools for getting the job done right. These frameworks are largely used by companies like LiquidWeb, and these are just some examples of their effective GPUs:

• The L4 ADA. These chips are NVIDIA’s options for AI inference and real-time analytics, which are much-needed in medical imaging. They come as standard options in many cloud-based GPU solutions based on NVIDIA’s chips.
• L 40 ADA S. The ADA L 40S is capable of large-scale data batch processing with boosted memory properties.
• The H100 NVL. For even larger workloads, which are often required for medical imaging, the NVIDIA H100 NVL chip turns out to be the ultimate option. It comes as a dual-GPU solution with up to 188GB of memory, which is essential for processing large datasets, especially with three-dimensional representations, such as cardiac imaging.

Conclusion

Ultimately, the use of deep learning in medical imaging is still in its early stages, and we can expect it to grow in the future. Progressive model training is the key here, and that takes time and computational power, which is where cloud-based GPU solutions come into play.

With them, medical institutions don’t need to deal with infrastructural requirements and challenges, including system maintenance and updating the chip roster. They come with the much-needed scalability, so providers like LiquidWeb could prove to be crucial for the new dawn of medical analysis and imaging. This would lead to higher efficiency, which directly translates to saving more lives.

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