Perhaps the most important application of next gen cloud technology in healthcare is AI. Medical teams and facility managers depend on artificial intelligence, so they need high performance computing networks to support this functionality. This is where the next gen cloud comes in, solving a range of problems and laying the groundwork for AI deployment.
We know that AI computing systems need to be smart. We also know that they need to draw upon vast amounts of data, interpret this data, and then make decisions accordingly. So it makes sense that effective AI deployment requires networks with high computing performance levels, facilitating an efficient flow of information from the server.
But this is a fairly simplistic way of looking at it. There are several other issues that need to be addressed before medical teams can get the best from AI.
• One: Artificial Intelligence Works in a Similar Way to Human Intelligence
While AI is certainly different from natural, organic intelligence, some of the key principles are the same. Just as humans can learn by a process of trial and error – seeing what works and what does not, and building an empirical resource of experience – artificial intelligence does the same.
This has a big impact on how AI computing systems use data. Smart applications and algorithms need to be "trained." They need to learn from the data and from the experience they gain as they operate, even adding to the data store as they work. The process is more complex than simply connecting AI components to data sources – high performance computing systems are required, achieving a multidirectional data flow and facilitating training.
• Two: AI Application and Algorithm Deployment Must Cover a Wide Area
AI applications and algorithms are relatively easy to run in a controlled, centralized environment. In more complex healthcare facilities, however, there are significant challenges. Healthcare professionals need to use these tools and features out in the field, potentially over a larger geographical area. This in turn necessitates a broad and highly capable computing network.
• Three: It's Not Always Feasible to Run On-Premises Operations
Investing in server hardware, and installing it on-premises, is expensive. Maintaining this hardware over a longer period of time is also expensive and time-consuming, and scaling it to meet evolving needs may be beyond the capabilities of many healthcare providers. Instead, facility managers need access to off-premises servers and data storage systems, which are maintained and scaled by external providers.
The Solution: High Computing Performance Cloud solutions
The next gen cloud offers a viable solution to each of these three considerations.
• Cloud systems provide the strong, multidirectional data connections required for training and teaching AI computing applications and algorithms.
• Networks and components can connect with the cloud remotely, facilitating easy deployment of AI tools.
• Healthcare facilities can use on-demand cloud resources, paying for what they need and what they use – meanwhile, storage, maintenance, and scaling take place off-premises.
Thanks to cloud technology, the bar of entry is significantly lowered for healthcare facilities. This means more medical professionals, and therefore more patients, can gain access to AI computing solutions and leverage the benefits that these solutions bring.