Although the healthcare industry has used distributed computing networks to tackle large-scale health challenges before—such as the [email protected] project, which allows individual PC users to contribute unused computing cycles to study how protein misfolding can lead to disease—there are a number of benefits to using a modern cloud computing solution. Cloud-based artificial intelligence (AI) and machine learning (ML) tools, for example, are helping healthcare organizations become more efficient and medical researchers develop better treatments for diseases.
Here are a few ways the cloud is powering healthcare research that’s leading to new cures, while also making the industry more efficient and secure.
Curing Alzheimer’s and Parkinson’s using the cloud
Together, Alzheimer’s and Parkinson’s afflict over 50 million people worldwide. Although the complexity of these disease processes has made finding cures elusive, the cloud’s ability to effortlessly scale resources and instantly tap into significant amounts of computing power is helping researchers gain a better understanding of these diseases faster.
One example: Researchers have built a comprehensive digital model of the human neurological system at comparatively low cost through quicker, on-demand access to high-performance technology stacks. Biotech startup NeuroInitiative uses virtualized GPUs in the cloud to create a general model of the nervous system, a strategy that has proved 40 times faster than using physical GPUs. “The need for lots of GPUs in a pay-as-you-go, easy-to-use model led us to the cloud from day one,” said Andy Lee, co-founder of NeuroInitiative. “We can spin up a 100,000-core cluster in minutes and stop paying for it after experiments finish.”
Using this technology, researchers have been able to simulate a variety of different treatments for neurological diseases. NeuroInitiative alone has identified more than 25 promising drug targets for Parkinson’s. They’ll be ready for human clinical trials in two to three years—half the time it typically takes for preclinical work.
Speeding up treatments with AI and ML
As more healthcare organizations embrace digital technology, they are dealing with increasing volumes of data, which must be quickly processed and analyzed in order to be meaningful. This is just the sort of work cloud-based AI and ML tools are designed to do. Just like on-premises AI and ML systems, these cloud-based tools can help humans handle routine and time-consuming tasks with unmatched speed and accuracy. Plus, these tools don’t require any special knowledge or equipment to set up, making data processing more cost-effective.
However, there’s more to be saved than money. When it comes to the development and distribution of new treatments, fast data analysis can also help combat diseases and save lives. Genomics researchers at the Icahn School of Medicine at Mount Sinai, for example, needed a way to analyze and sequence large sets of genomic data with a limited set of resources. Using cloud-based AI tools from Microsoft Genomics, as well as Azure Data Lake Analytics, they were able to download their data, and then use AI to quickly process and archive it. “The tool can uniformly realign everything and let me do the variant calling for the analysis I want,” said Dr. Robert Klein, head of the Klein Lab at the Icahn School. They are scaling up their research as a result.
Likewise, Intelligent Retinal Imaging Systems (IRIS) wanted to build a platform that could detect diabetic retinopathy, a form of vision loss that can develop rapidly. To help make tests more accessible, they designed a system doctors can use to quickly analyze images of retinas and detect anomalies using ML algorithms. All doctors have to do is send a retinal image to IRIS, which then processes it using Azure Service Bus and Azure Machine Learning Package for Computer Vision. Within 24 hours, doctors receive an enhanced image back with anomalies identified, making it easy for them to give a final diagnosis. “We went from zero to 300,000 patients examined in under five years,” said Jonathan Stevenson, chief strategy and information officer at IRIS. “There is no way we could have done that without Azure.”
Automatically securing healthcare data
Privacy and security have long been concerns in healthcare, and they’ve taken on a much larger significance with the digitization of the industry. Organizations that want to modernize their operations without sacrificing security can use the cloud as an alternative to siloing all their patient records on site.
One of the most effective ways to do this is by choosing a cloud solution that comes with HIPAA and HITRUST compliance. Instead of having to worry about properly storing, accessing, and analyzing sensitive health data, an organization can simply transfer patient information to their cloud, where it will automatically adhere to secure data regulations. Another layer of protection is to use cloud services that emphasize security from the ground up. This includes introducing security elements at every phase, from the initial hardware components to the final transfer of information.
Cloud technology means better treatments are on the way for some of humanity’s most difficult diseases. By giving researchers easy and scalable access to computing resources, cloud technology is helping to reduce the time it takes to test hypotheses, increase the iteration of promising ideas and treatment methods, and develop new cures. It’s also allowing healthcare organizations to modernize without neglecting security. All this is helping to make medicine more effective, accessible, and timely. For doctors, researchers, and patients, the future of healthcare is in the cloud.